Method and apparatus for matching vehicle ecu programming to current vehicle operating conditions

ABSTRACT

Disclosed herein are techniques for implementing vehicle ECU reprograming, so the ECU programming, which plays a large role in vehicle performance characteristics, is tailored to current operational requirements, which may be different than the operational characteristics selected by the manufacturer when initially programming the vehicle ECU (or ECUs) with specific instruction sets, such as fuel maps. In one embodiment, a controller monitors the current operational characteristics of the vehicle, determines the current ECU programming, and determines if a different programming set would better suited to the current operating conditions. In the event that the current programming set should be replaced, the controller implements the ECU reprogramming. In a related embodiment, users are enabled to specify the ECU programming to change, such as changing speed limiter settings.

RELATED APPLICATIONS

This application is based on a prior copending provisional applicationSer. No. 61/618,827, filed on Apr. 1, 2012, the benefit of the filingdate of which is hereby claimed under 35 U.S.C. §119(e).

BACKGROUND

Modern vehicles are often equipped with sophisticated controllers thatenable vehicle performance characteristics to be optimized for specificneeds. An engine manufacturer may use different programming to logic tovary the engine performance characteristics, including horsepowerdelivered, according to the needs of a specific customer or class ofcustomers. For example, trucks sold for use in over the road trucking,operating for most of their service life on highways, require differentperformance characteristics than similar trucks operating for most oftheir service life on city streets in stop-and-go traffic. A fuel maprefers to a set of programming instructions that can be input into anengine control unit (an ECU) to modify performance characteristics of anengine.

As used herein and in the claims that follow, the term fuel map refersto a specific program (i.e., a set of machine instructions) used by anengine control unit (ECU) to determine how to respond to various sensorinputs (i.e., changes in driving conditions). The ECU generally respondsto changing inputs by changing at least one of the following parameters:fuel flow rate, spark timing, and idle speed. Changing the fuel map(i.e., the instruction set used by the ECU) will change the performancecharacteristics of the engine. Manufacturers generally select a fuel mapto provide satisfactory vehicle performance over a wide range ofconditions.

Other ECU programming instructions sets can be used to modify otherperformance characteristics, such as maximum road speed, maximum RMP,maximum idle time, etc.

In general, modification of such programming instructions sets requiresa replacement instruction set, a hardware interface to be coupled to avehicle data port (enabling the instruction set to be sent to theappropriate ECU), and a software interface or software application tomanage the replacement. Some third party vendors sells kits enablingvehicle owners to perform their own ECU reprograming using a laptop anda custom hardware interface, programming set, and software application(generally the hardware interface, programming set, and softwareapplication are sold together as a kit). Otherwise, vehicle operatorsneed to bring their vehicle to a mechanic to have such ECU reprogramingperformed.

It would be desirable to provide vehicle operators with the ability tomore readily implement ECU reprograming. Fuel mapping and otherperformance related instructions set, customized to the specificperformance requirements of a vehicle for a specific route or trip, maylead to more cost efficient operations.

SUMMARY

One aspect of the novel concepts presented herein is a method ofenabling vehicle operators to more readily implement ECU reprograming,so that the operator can tailor their vehicle's current ECU programingto current operational requirements, which may be different than theoperational characteristics selected by the manufacturer when initiallyprogramming the vehicle ECU (or ECUs) with specific instruction sets,such as fuel maps.

In one embodiment, a controller monitors the current operationalcharacteristics of the vehicle, determines the current ECU programming,and determines if a different programming set would be better suited tothe current operating conditions. In the event that the currentprogramming set should be replaced, the controller implements the ECUreprogramming. In at least one embodiment, that controller is at thevehicle, while in at least one other embodiment the controller is partof a remote computing system logically connected to the vehicle via awireless data link.

Significantly, the monitoring of vehicle operational data (the termvehicle operational data includes, but is not limited to; vehicle speed,vehicle location, engine RPMs, engine load, vehicle mass, enginetemperature, coolant temperature, engine oil temperature, braketemperature, tire pressure, tire temperature, and fuel use, noting thatsuch parameters are exemplary and not limiting) is done in real-time,either via a controller at the vehicle or a controller remote from thevehicle (where operational data is conveyed from the vehicle to theremote controller in real-time). The term real-time as used herein andthe claims that follow is not intended to imply the data is analyzed ortransmitted instantaneously, rather the data is collected over arelatively short period of time (over a period of seconds or minutes),and analyzed (or transmitted to the remote computing device on anongoing basis and analyzed) in a compressed time frame, as opposed tostoring the data at the vehicle or remotely for an extended period oftime (hour or days) before analysis.

In at least one embodiment, the vehicle is equipped with a positionsensor, such as a Global Position System (GPS) device. A controller,either part of the GPS device or another controller at the vehicle usesGPS derived slope data to determine a vehicle's mass, and then uses thevehicle mass data to determine if the current vehicle ECU programming isappropriate. For example, a vehicle operating with a relatively lightload may be operated more efficiently using a first set of ECUprogramming (i.e., a first fuel map), whereas a vehicle operating with arelatively heavy load may be operated more efficiently using a secondset of ECU programming (i.e., a second fuel map). Using vehicle massdetermined while the vehicle is operating based on GPS derived slopedata represents one input that can be used to determine if current ECUprogramming is appropriate. In the event that the current programmingset should be replaced, the controller implements the ECU reprogramming.

Fundamentally, GPS systems calculate velocity in three components (X, Y,Z or N/S, E/W, and Up/Down) based on a Doppler shift of the GPSsatellite signals. Scalar speeds can then be calculated from those threecomponents. For example, absolute speed or actual vehicle speed can bedetermined, as well as ground speed based on the shortest distancebetween two points (i.e., based on distance as the crow flies).Horizontal ground speed (V_(HGS)) can be calculated using thePythagorean Theorem. To calculate a grade (G) the vehicle is travelingover (as a percentage), one can take the Z/Up magnitude and divide it bythe horizontal ground speed. Replacing Z, x and y with directionalvectors (such as Up for Z, West for x and North for y, recognizing thatsuch directional vectors are exemplary, and may change based on theactual GPS data collected from the vehicle) enables one to calculateslope. The slope data is then used to determine the mass of the vehicleat that time. Pervious techniques to calculate mass use torque output,engine RPMs, and vehicle velocity to calculate a vehicle's mass orweight, but did not factor in slope, and thus are not accurate overroutes including variable slopes (which most routes include). Animproved mass metric (by including the GPS derived slope data in a masscalculation) enables a more accurate vehicle weight to be provided.

Note the calculation of vehicle mass using GPS derived slope data can beperformed a plurality of times during a specific vehicle trip, andchanges in the vehicle mass over time (due to partial unloading or fuelconsumption) could trigger additional ECU reprogramming.

Another input that can be used by the controller monitoring the currentvehicle ECU programming is vehicle load data entered into an inputdevice by a driver of the vehicle. Such vehicle load data can be basedon a vehicle weight provided by a scale, or can be made available onshipping documentation provided to the driver when picking up a load, orcan be provided to the driver via a communication from a dispatcher,agent or customer having access to the data defining the load beingpicked up by the vehicle. The driver will use an input device to providethe vehicle load data to the controller, which then uses the vehicleload data to determine if the current vehicle ECU programming isappropriate. As noted above, using a different fuel map may result inmore efficient vehicle operation when a fuel map is correlated to theactual load of the vehicle. Again, the controller will compare thecurrent ECU programming to the loaded state of the vehicle and availableECU programming sets, and in the event that the current programming setshould be replaced, the controller implements the ECU reprogramming.

Still another input that can be used by the controller monitoring thecurrent vehicle ECU programming is vehicle load data entered into aninput device by a dispatcher or agent at a location remote from thevehicle. In such a case, the controller needs to be coupled to theremote input via a wireless data link, so that vehicle load data inputfrom a remote site can be conveyed to the controller at the vehicle. AGSM or other type of cellular modem can be employed as such a data link(noting that such a data link is exemplary, and not limiting, and otherwireless data links, including satellite based data links, can also beemployed). Such remotely input vehicle load data can be based oninformation provided to a dispatcher or broker coordinatingtransportation of a load by the vehicle in question. The controller usesthe remotely input vehicle load data to determine if the current vehicleECU programming is appropriate, generally as discussed above.

Still another input that can be used by the controller monitoring thecurrent vehicle ECU programming is vehicle routing data. When a vehicleroute is known in advance, an analysis of the route can be performed tooptimize ECU programming parameters based on the route characteristics.For example, where a first portion of the route involves mountainousterrain, and a second portion of the route involves relatively flatterrain, a first set of ECU programming may provide more efficientvehicle operation for the first portion of the route, while a second setof ECU programming may provide more efficient vehicle operation for thesecond portion of the route. The analysis of the route and the selectedECU programming parameters may be based on empirical data collectedduring previous trips over the same route, or may be based on knowledgeabout the terrain, or combinations thereof. Particularly where a routeis repeatedly traversed under similar load conditions, a carrier mayvary ECU parameters on different trips while collecting empirical data,so the most efficient ECU parameters can be determined, and used forfuture trips. The controller at the vehicle tasked with ECUreprogramming can use ECU programming assigned to specific portions ofthe route, and GPS data collected during operation of the vehicle, tovary the ECU parameters based on the location of the vehicle.

In a related embodiment, the controller at the vehicle that monitors ECUprogramming states is logically coupled to a GPS device (or otherposition sensing system) in the vehicle, such that the controllerchanges the ECU programming based on the GPS location of the vehicle.Predefined ECU programming parameters can be assigned to downhillsegments, to uphill segments, to specific altitudes, to relatively flatterrain, and to route segments where speed and heading may remainconstant for extended periods (such as long stretches of highway).

It should be understood that in addition to GPS parameters, thecontroller determining which ECU programming parameters are appropriatefor a vehicle based on current operational data inputs can also useother types of data input, such as ambient temperature, time, and date.For example, empirical data or user knowledge might indicate that afirst set of ECU programming may lead to more efficient vehicleoperation when the ambient temperature is relatively low, while a secondset of ECU programming may lead to more efficient vehicle operation whenthe ambient temperature is relatively high. The ambient temperature canbe measured by a sensor in the vehicle, or the ambient temperature canbe estimated remotely and conveyed to the vehicle (such as by a weatherreporting/predicting service). Similarly, empirical data or userknowledge might indicate that a first set of ECU programming may lead tomore efficient vehicle operation during nighttime vehicle operation,while a second set of ECU programming may lead to more efficient vehicleoperation during daylight vehicle operation. Daylight/nighttimeconditions can be determined remotely and conveyed to the vehicle, orcan be measured at the vehicle using light sensors and/or clocks.Similarly, empirical data or user knowledge might indicate that a firstset of ECU programming may lead to more efficient vehicle operationduring a first season (i.e., summer, fall, winter, spring), while asecond set of ECU programming may lead to more efficient vehicleoperation during a different season. The current season can bedetermined remotely and conveyed to the vehicle, or can be measured atthe vehicle using a clock/calendar function.

Other operational data that can be used as an input for the controllertasked with reprogramming the vehicle ECU to enhance vehicle efficiencyincludes engine load (a parameter based in part on vehicle weight andengine RPMs), engine RPMs, engine oil temperature, engine coolanttemperature, vehicle speed, transmission gear selection, vehicle weight,cruise control status, accessory device status (such as the use ofsupplementary cooling fans or power take off units). Differentcombinations and permeations of such inputs may change the optimal ECUprogramming selected by the controller. The assignment of optimal ECUprogramming sets to specific combinations of operation data inputs canbe based on empirical data or user knowledge, as well as combinationsthereof.

The status of a power take off unit is a special case that maysignificantly alter the ECU programming associated with optimalefficiency. For example, a power take off unit is used when the enginein the vehicle is not being used to generate horsepower to move thevehicle over the road, but rather to generate horsepower to be used by amechanical or hydraulic accessory, or to generate electricity to drivean electrically energized accessory. Lift buckets, ladders, hoists areexemplary but not limiting types of accessory units associated with apower take off unit. Often the power required by such accessory units ismuch less than required for over the road operation, and such accessorycomponents may be used for extended periods of time. Changing ECUprogramming to optimize efficiency can result in significant performanceimprovements in fuel consumption, particularly where requiredperformance characteristics for over the road operation vary widely fromthe required performance characteristics for power take off operation.In some jurisdictions, PTO unit use may not trigger the same emissioncontrol requirements, such that it may be possible to bypass emissioncontrol systems during PTO, generally for enhanced fuel economy.

While fuel maps have been discussed above as a parameter that can bemodified by ECU reprogramming, it should be understood that otherparameters can also be modified in accord with the concepts disclosedherein. For example, ECU parameters controlling vehicle shiftingpatterns can be similarly modified. Different operational inputconditions can result in changes to one or more ECU parameters,including but not limited to fuel maps and shift patterns.

In at least some embodiments, ECU programming changes can be done duringvehicle operation. In other embodiments, a driver interface component isused to alert the driver that an ECU programming change is required, andthe driver will be trained to respond to such an alert by pulling overat a safe location to shut down the vehicle (or idle the vehicle) whilethe programming change is carried out. Whether or not ECU programmingchanges are performed during active vehicle operation, vehicle shutdone, or vehicle idle will sometimes be based on operator policy (someoperators may demand such changes be done at idle or while the vehicleis shut down for safety reasons), and will sometimes be based on thedesign parameters of the specific ECU being reprogrammed.

In at least one embodiment, the ECU reprogramming is related to avehicle speed limiter. Some jurisdictions have different speed laws. Insuch an embodiment, a driver or dispatcher can convey a command to thecontroller at the vehicle managing the ECU reprogramming to instruct achange in the speed limiter settings. Thus, if a driver (or dispatcher)knows the vehicle is approaching a locality with different speed rules,the driver (or dispatcher, via a remote data link) can instruct thecontroller to initiate such a change. For example, when a vehicleoperating in the US enters Canada, failing to change the speed limitingsettings can lead to a fine. Providing an owner/operator or driver withthe ability to correct or change the settings when needed will aid incompliance, and reduce liability for drivers. In one related embodimentthe driver will have the ability to make an ECU programming regardingspeed limit settings by inputting a command in an input device in thevehicle, where the input device is coupled to the controller at thevehicle. In a related embodiment, a third party can remotely access thecontroller that is able to effect an ECU programming, and will effectsuch a programming change when requested to do so by a driver ordispatcher (the term dispatcher being intended to encompass anyindividual authorized to request such a change on behalf of the operatorof a vehicle, regardless of their actual title or job duty). In anexemplary embodiment, the third party offers telematics services to thevehicle operator, such as GPS data collection and storage.

This Summary has been provided to introduce a few concepts in asimplified form that are further described in detail below in theDescription. However, this Summary is not intended to identify key oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

DRAWINGS

Various aspects and attendant advantages of one or more exemplaryembodiments and modifications thereto will become more readilyappreciated as the same becomes better understood by reference to thefollowing detailed description, when taken in conjunction with theaccompanying drawings, wherein:

FIG. 1 is a high level flow chart showing the overall method stepsimplemented in accord with one exemplary embodiment for achieving theconcepts disclosed herein;

FIG. 2 is a more detailed flow chart showing method steps implemented inan exemplary preferred embodiment;

FIG. 3 schematically illustrates a vehicle that includes a plurality ofsensors configured to collect the required metrics;

FIG. 4A is a functional block diagram illustrating the functionalelements of an embodiment in which the metrics are processed within thevehicle to obtain the driver's performance ranking, for example, inreal-time;

FIG. 4B is a functional block diagram illustrating the functionalelements of an embodiment in which the metrics are processed by acomputing device remote from the vehicle to obtain the driver'sperformance ranking;

FIG. 5 schematically illustrates the interior of a vehicle configuredwith a display to provide the driver with the performance ranking inreal-time;

FIG. 6 schematically illustrates a vehicle that includes a GPS unitconfigured to collect GPS data that can be used to provide a pluralityof metrics for use in determining a driver performance ranking in accordwith one aspect of the concepts disclosed herein;

FIG. 7 is a flow chart showing method steps implemented in an exemplarypreferred embodiment, where GPS data are used to provide a plurality ofmetrics used to determine the driver's performance ranking;

FIG. 8 is a flow chart showing method steps implemented in accord withone aspect of the concepts disclosed herein, the method stepsrepresenting an exemplary technique used to implement that aspect, theaspect comprising using GPS or position data are used to determine aslope the vehicle is traveling over (in at least one embodiment, theslope data will in turn be used to calculate an accurate vehicle massmetric);

FIG. 9 is a functional block diagram graphically illustrating forcevectors acting on a vehicle, and how those vector can be used to solvefor vehicle mass, where the GPS derived slope represents a uniquemetric;

FIG. 10 is a flow chart showing exemplary method steps implementedaccording to one aspect of the concepts disclosed herein, where GPS orposition derived slope data is used to calculate a vehicle's mass at aplurality of intervals during the operation of a vehicle, and then usingthe vehicle mass to determine a cost per loaded mile;

FIGS. 11A-11C graphically illustrate vehicle performance histogramsgenerated derived in part using the GPS derived slope data of FIG. 8;

FIGS. 12A-12B graphically illustrate vehicle performance histogramsgenerated derived in part using the GPS derived slope data;

FIG. 13 is a flow chart showing exemplary method steps implementedaccording to one aspect of the concepts disclosed herein, in which apromotional driver performance campaign is implemented at a hostedwebsite;

FIGS. 14 and 15 are functional blocks diagram illustrating that thepromotional driver performance campaigns and bonuses of the method ofFIG. 13 can include drivers from a single fleet (intra-company) ordrivers from multiple fleets (inter-company);

FIG. 16 is a functional block diagram illustrating exemplary elements ina vehicle/driver performance monitoring system in accord with one aspectof the concepts disclosed herein;

FIG. 17 is an exemplary screen shot of a website hosting a promotionaldriver performance campaign;

FIG. 18 is a another functional block diagram illustrating exemplaryelements in a vehicle/driver performance monitoring system in accordwith one aspect of the concepts disclosed herein;

FIG. 19 is an exemplary computing environment for implementing some ofthe concepts disclosed herein;

FIG. 20 is a functional block diagram of an exemplary telematics deviceadded to an enrolled vehicle to implement one or more of the methods ofFIGS. 1, 2, 7, 8 and 10;

FIG. 21 is a functional block diagram of an exemplary vehicle componentsemployed to implement the ECU reprogramming in response to currentoperational data inputs concept disclosed herein;

FIG. 22 is a flow chart showing exemplary method steps implementedaccording to one aspect of the concepts disclosed herein, in whichvehicle ECU programming is modified in response to one or more currentvehicle operational data inputs; and

FIG. 23 is a flow chart showing exemplary method steps implementedaccording to one aspect of the concepts disclosed herein, in whichvehicle ECU programming is modified in response to a specific userrequest for a programming change.

DESCRIPTION Figures and Disclosed Embodiments are not Limiting

Exemplary embodiments are illustrated in referenced Figures of thedrawings. It is intended that the embodiments and Figures disclosedherein are to be considered illustrative rather than restrictive. Nolimitation on the scope of the technology and of the claims that followis to be imputed to the examples shown in the drawings and discussedherein. Further, it should be understood that any feature of oneembodiment disclosed herein can be combined with one or more features ofany other embodiment that is disclosed, unless otherwise indicated.

Newly Disclosed Subject Matter

The concepts disclosed herein relate to both newly disclosed subjectmatter and subject matter presented in a previously filed andunpublished application. The previously filed but not published subjectmatter provides contextual information that is relevant to the newdisclosure, hence it inclusion. The newly disclosed subject matterbegins with FIG. 21.

Exemplary Logic for Determining Driver Performance

FIG. 1 is a high level flow chart showing the overall method stepsimplemented in accord with one aspect of the concepts disclosed herein.In a block 10 a plurality of metrics related to driver performance areautomatically collected by a plurality of sensors incorporated into avehicle. Such metrics generally relate to driver operation of thevehicle, but may also simply include data related to the vehicle. Suchmetrics can include, but are not limited to, vehicle speed, vehicleacceleration, vehicle deceleration, engine RPMs, idle time, enginetemperature, coolant temperature, oil temperature, fuel consumption, andvehicle positional data. Those of ordinary skill in the art will readilyrecognize that many different metrics related to vehicle performance anddriver performance can be collected. Thus, it should be recognized thatthe specifically identified metrics are intended to be exemplary, ratherthan limiting. In a block 12, a numerical ranking of the driver'sperformance is determined based on at least some of the metricscollected.

FIG. 2 is a more detailed flow chart showing method steps implemented ina preferred embodiment, providing additional details as to how thenumerical ranking of the driver's performance can be determined. In ablock 14, a numerical value is assigned to each metric collected. Itshould be recognized that plurality of valuation schemes can beimplemented, and the specific scheme implemented is not critical. Itshould also be recognized that a fleet operator can perceive somemetrics to be more or less important to overall driver performance.Thus, individual metrics can be weighted differently. For example, onefleet operator may have little tolerance for drivers who exceed postedspeed limits and want to place great emphasis on this metric whendetermining the numerical ranking. Such a fleet operator can assignsignificantly more weight to the detection of a driver exceeding a speedlimit than to the detection of a driver incurring excessive idle time.Regardless of the specific valuation scheme implemented, a numericalranking will be determined for each metric collected. In a block 16, thenumerical rankings for each metric are combined. In a block 18, thecombined numerical values for each metric are normalized, to enableperformance rankings for different drivers to be more equitablycompared. In one embodiment, the normalization is based on a distanceover which a driver has operated a vehicle. In another embodiment, thenormalization is based on an amount of time the driver has operated avehicle. This normalization enables the output of the normalizedcombined total to be provided as a numerical ranking in a block 20indicating a driver's performance. Note that the valuation schemeimplemented will determine whether a specific numerical value isindicative of a relatively good performance or a relatively poorperformance. Under some valuation schemes, relatively higher combinedand normalized numerical rankings are generally indicative of relativelybetter driver performance. In other valuation schemes, relatively lowercombined and normalized numerical rankings are generally indicative ofrelatively better driver performance.

FIG. 3 schematically illustrates a vehicle including a plurality ofsensors configured to collect the required metrics. A vehicle 22, suchas a bus or a truck, includes a plurality of sensors 24 a-24 h. Itshould be recognized that the specific number of sensors, and thespecific types of sensors and types of data collected by the sensors,are not critical, so long as the sensors collect data for the desiredmetrics. As noted above, a plurality of different metrics have beenspecifically identified, however it should be recognized that suchmetrics are intended to be exemplary, and not limiting on the conceptsdisclosed herein. In the disclosed exemplary embodiment, each sensor iscoupled to a CPU 26 (which, as described in greater detail below, may insome of embodiments be replaced with (or provided in addition to) atransmitter).

FIG. 4A is a functional block diagram 28 a illustrating the functionalelements of an exemplary embodiment in which the metrics are processedwithin the vehicle to obtain the driver's performance ranking. Thevehicle is equipped with sensors 30 configured to collect the requiredmetrics. The sensors are logically coupled with an onboard vehicle CPU34, which is configured to implement the method steps generallydescribed above. Onboard CPU 34 is logically coupled to a memory 32 inwhich are stored the machine instructions that are executed by theonboard CPU 34 to carry out these logical steps. The plurality ofmetrics collected by sensors 30 can also be stored in memory 32. A(preferably optical or wireless) transmitter 36 (or other data link) canbe included to enable either the plurality of metrics or the driver'sperformance ranking to be communicated to a remote computing device. Anoptional display 38 can be included in the vehicle to provide real-timefeedback to the driver (by displaying the driver's performance rankingin real-time). As discussed above, if display 38 is implemented, it isdesirable to provide the ability for the driver to determine whichmetrics are having the most impact on the driver's performance ranking.

FIG. 4B is a functional block diagram 28 b illustrating the functionalelements of an exemplary embodiment in which the metrics are processedby a computing device to obtain the driver's performance ranking, wherethe computing device is remote from the vehicle. Once again, the vehicleis equipped with sensors 30 configured to collect the required metrics.The sensors are logically coupled with an onboard vehicle CPU 34, whichis configured to transmit the collected metrics to remote computingdevice 39 through transmitter 36 (or other data link). In a particularlypreferred embodiment, transmitter 36 is a wireless transmitter. In suchan embodiment, the method steps generally described above for processingthe collected metrics can be executed by the remote computing device.Onboard CPU 34 is logically coupled to memory 32 in which the collectedmetrics can be stored, if the metrics are not to be transmitted to theremote computing device in real-time. Even if the metrics aretransmitted to the remote computing device in real-time, such metricscan be stored in memory 32 as a backup in case the transmission is notsuccessful. In such an embodiment, a display is not likely to bebeneficial, unless the remote computing device is configured to transmitthe calculated performance ranking back to the vehicle for display tothe driver.

FIG. 5 schematically illustrates the interior of a vehicle configuredwith a display 40 to provide the driver with a performance ranking inreal-time. As discussed above, such a display can be implemented by theembodiment schematically illustrated in FIG. 4A. While FIG. 5 shows asingle numerical performance ranking being dis-played, it should beunderstood that the concepts disclosed herein encompass displaying aplurality of different metrics (at one or in rotation), as well asdisplaying a cost per loaded mile metric, which is discussed in detailbelow in connection with FIG. 10. The cost per loaded mile metric can becalculated using the concepts disclosed herein at a remote computingdevice and conveyed back to the vehicle for display, or can becalculated using a processor in the vehicle.

FIG. 6 schematically illustrates a vehicle 22 a that includes a GPS unit44 configured to collect GPS data that can be used to determine aplurality of metrics for use in determining a driver performanceranking. Such an embodiment enables the driver performance rankingdiscussed above to be generated without requiring individual oradditional sensors to be integrated into the vehicle (although it shouldbe recognized that such individual sensors could be used in addition to(or as an alternative source of) the data provided by the GPS unit, toprovide additional metrics used in determining a driver's performanceranking, generally consistent with the method steps described above withrespect to FIGS. 1 and 2). Vehicle 22 a, such as a bus or a truck (orautomobile, or construction equipment, generally as described above)includes GPS unit 44 coupled with an ignition system 42 of the vehicle.In an exemplary embodiment, the GPS unit will be coupled with theignition switch, such that it is assumed that when the ignition switchis on, the engine of the vehicle is actually running, and the GPS unitwill be activated. As described in greater detail below, GPS data can beused for a plurality of metrics, including idle time, deceleration timeand magnitude, acceleration time and magnitude, and to determine if adriver has violated a speed limit. The most basic GPS unit is able todetermine a position of the vehicle at a current time. That positionalinformation can be used to calculate the speed of a vehicle bydetermining the change in position of the vehicle between two successivepoints in time, and to calculate the acceleration or deceleration of thevehicle by determining the change in speed of the vehicle over a timeincrement. More typically, GPS units automatically determine position,speed, and acceleration/deceleration internally, and these metrics wouldthen not need to be determined by an external computing device (remoteor local).

GPS unit 44 preferably includes or is connected to a wirelesstransmitter (not separately shown), such that the GPS data can bewirelessly transmitted to a remote computing device, preferably inreal-time. The remote computing device can be programmed to manipulatethe GPS data to determine a plurality of metrics, which can then be usedto calculate a driver's performance ranking, generally as describedabove. It should be recognized that as an alternative, GPS unit 44 caninclude an onboard memory, such that the GPS data are stored in the GPSunit, to be uploaded to a remote computing device at a later time (forexample, using a wireless or hardwired data link). Significantly, GPSunit 44 enables driver performance rankings to be determined, even ifthe vehicle is not equipped with separate other sensors of the metricdata or an onboard computer (as are required in the embodiments of FIGS.3, 4A, and 4B). It should be understood that the concepts disclosedherein encompasses coupling such a GPS unit to vehicle sensors and/or avehicle data bus, such that driver/vehicle performance data collected byother vehicle sensors can be combined with GPS data and conveyed to aremote computing site. While not specifically shown in FIG. 6, it shouldbe understood that GPS unit 44 can include a processor that uses GPSdata and sensor data collected from the vehicle to calculate performancemetrics, which are then combined with GPS data and conveyed to theremote computing site. One such metric is GPS derived slope data,discussed in detail in below in connection with FIG. 8. Such performancemetrics calculated by a processor in the vehicle (whether or not thatprocessor is associated with a GPS unit, or is a separate processor inthe vehicle) can be displayed in the vehicle, as well as (or in lieu of)being conveyed to a remote computing device.

FIG. 7 is a flow chart showing method steps implemented in one exemplaryembodiment when GPS data are used to calculate a plurality of metricsused to determine the driver's performance ranking. In a block 46, thevehicle ignition is switched on (and it is assumed that the engine isrunning), thereby powering on the GPS unit. In a block 48, the GPS unitcollects GPS data (information corresponding both to a particular pointin time and a specific geographical position that the vehicle occupiesat that specific point in time). In a block 50, the GPS data aretransmitted to a remote computing device. As noted above, the GPS dataare preferably transmitted to the remote computing device in real-time.However, it should be recognized that the GPS data can be temporarilystored within the GPS unit (or in a memory electronically coupled to theGPS unit), and transferred to the remote computing device at a latertime. In a block 52, the remote computing device uses the GPS data tocalculate an idle time metric. Because the GPS unit is only on when theignition switch is on and the engine of the vehicle is assumed to berunning, an assumption can be made that the idle time equals theaccumulated time that the GPS unit is on, but the vehicle is notchanging position.

In a block 54, the remote computing device uses the GPS data todetermine metrics corresponding to acceleration time and accelerationmagnitude. In a block 56, the remote computing device uses the GPS datato determine metrics corresponding to deceleration time and decelerationmagnitude. In a block 58, the remote computing device uses the GPS datato determine whether a driver has exceeded a speed limit. Those ofordinary skill in the art will readily recognized that several mappingdatabases exist that include a database of speed limits and which enablea speed limit for a specific portion of a route being driven by avehicle to be determined based on a particular geographical coordinateof the vehicle following that route. GPS data includes an indication ofthe speed of the vehicle at a particular time while the vehicle is at aparticular geographical location. Once the vehicle speed has beendetermined for a particular geographical position, a database can beconsulted to determine the speed limit associated with that positionalong the route, thereby enabling a determination to be made as towhether the driver has exceeded the speed limit. In a block 60, theplurality of metrics calculated from the GPS data are used to determinethe driver's performance ranking, generally as described above inconnection with FIGS. 1 and 2.

It should be recognized that the GPS data can be used to calculate fewermetrics than those described above in connection with FIG. 7, and thatthe metrics specifically identified in FIG. 7 are intended to beexemplary, rather than limiting. Furthermore, if the vehicle includesother sensors for determining metrics, the sensor data can also beforwarded to the remote computing device to be used in calculating thedriver's performance ranking, also generally as described above.Furthermore, it should be recognized that rather than (or in additionto) transmitting the GPS data to a remote computing device, the GPS datacan be conveyed to a computing device on the vehicle, for determiningthe driver's performance ranking.

Preferably, performance rankings are determined for each driver in acompany (or each driver of a vehicle for which driver performance is ofinterest), and posted publicly so that drivers can compare each other'sindividual performance rankings The public display of driver performanceis expected to provide an incentive for drivers to improve their drivingperformance rankings.

It should be understood that the concepts disclosed herein encompassmany differ types of performance metrics, and different techniques forcollecting them. In at least some embodiments, as exemplified by FIG. 6,the performance metrics are transmitted from the vehicle to a remotecomputing device by a wireless data link enabled GPS unit (such as aGSM/GPS), that also collects location data during the vehicle'soperation. It should be understood that such performance metrics canalso be collected using a data recorder that does not have wireless datatransmission capability, such that data will need to be exported fromthe data recorder to a remote computing device periodically, or the datarecorder will need to be removed from the vehicle and coupled to acomputing device periodically to export the data used to calculate thedriver performance metric(s).

While specific parameters or metrics used to derive a driver performancemetric have been discussed above, it should be recognized that thefollowing different parameters/metrics are specifically encompassedherein. One or more embodiments in which the performance metric is basedat least in part from data collected from one or more engine controlunits (or vehicle computer) in a vehicle operated by the driver whoseperformance is being measured. One or more embodiments in which theperformance metric is based at least in part on fuel economy. One ormore embodiments in which the performance metric is based at least inpart on carbon footprint reduction. One or more embodiments in which theperformance metric is based at least in part on minimizing fuelefficiency robbing behavior, including sudden braking, rapidacceleration and downshifting too early. One or more embodiments inwhich the performance metric is based at least in part on maximizingfuel efficiency enhancing behavior, including coasting to a stop(instead of staying on the accelerator until the last minute and thenbraking hard), high average vehicle speeds with minimum time spent atmaximum vehicle speed, high percent trip distance in top gear (90+%recommended), high percent distance in cruise control, minimum percentidle/PTO operation, minimum service brake activity, low number of suddendecelerations, and low service brake actuation's/1000 miles.

Another aspect of the concepts disclosed herein is a technique tomonitor vehicle location data (i.e. GPS data) over time to determine theactual operating speed of a fleet vehicle. Many fleet operators have theability to define maximum speed parameters on their vehicles. Maximumspeed parameters are defined to enhance safety and to reduce fuel costs(statistics indicated that for heavy trucks every MPH over 62 MPHreduces fuel economy by 0.1 MPG). However, these speed settings can faildue to maintenance issues, or driver manipulations. The maximum speedsetting is based on understanding the size of the vehicle's tires. Ifduring maintenance a different size tire is used as a replacement, thepredefined speed settings will be inaccurate. Because drivers are oftenpaid by the mile, drivers have an incentive to defeat the maximum speedsettings, and drivers may encourage the use of different tire sizes, sothey can go faster than the maximum speed setting, to increase theirearnings. Drivers can also purchase and install aftermarket kitsdesigned to bypass speed governors, again so they can go faster than themaximum speed setting, to increase their earnings. The conceptsdisclosed herein encompass collecting GPS data during the operation of afleet vehicle, and analyzing the location and time parameters of thatdata to identify when a fleet vehicle exceeds a predefined maximumspeed. The GPS verified speed metric can be used as a driver performancemetric on its own, or be combined with other metrics to generate adriver performance metric.

Another aspect of the concepts disclosed herein is to monitor manualoverrides for cooling fans in fleet vehicles. Such cooling fans,generally controlled by a vehicle engine control unit (ECU) or vehiclecomputer, consume up to 50-70 HP, and measurably reduce fuel economy.Drivers who habitually override the automatic fan settings can consumeunnecessary amounts of fuel. Thus the concepts disclosed hereinencompass monitoring a driver's use of cooling fan manual override, tofacilitate an evaluation of a driver's performance, and to enabledrivers who use such overrides excessively to be identified and trainedto reduce their use of manual cooling fan overrides. The cooling fanmanual override metric can be used as a driver performance metric on itsown, or be combined with other metrics to generate a driver performancemetric.

Another aspect of the concepts disclosed herein is to monitor engineRPMs during a driver's operation of a vehicle. Over revving an enginecan lead to increased fuel use and engine wear. Drivers who habituallyover rev their vehicles engines can consume unnecessary amounts of fuel.Thus the concepts disclosed herein encompass monitoring the RPMparameters while a driver operates a vehicle, to facilitate anevaluation of a driver's performance, and to enable drivers whoconsistently over rev their vehicle's engines to be identified andtrained to reduce their over revving behavior. The over revving metriccan be used as a driver performance metric on its own, or be combinedwith other metrics to generate a driver performance metric.

Another aspect of the concepts disclosed herein is to monitor theshifting behavior during a driver's operation of a vehicle. Not runninga heavy truck in the highest possible gear when possible can lead toincreased fuel use and engine wear. Statistics indicate that every 10%drop of time in top gear results in a 0.5% mpg loss. Thus the conceptsdisclosed herein encompass monitoring shifting behavior while a driveroperates a vehicle, to facilitate an evaluation of a driver'sperformance, and to enable drivers who consistently under shift to beidentified and trained to reduce their over revving behavior. Theshifting pattern metric can be used as a driver performance metric onits own, or be combined with other metrics to generate a driverperformance metric.

Another aspect of the concepts disclosed herein is to monitor the amountif idle time during a driver's operation of a vehicle. Increased idleleads to increased fuel use and engine wear. Thus the concepts disclosedherein encompass monitoring idle time behavior while a driver operates avehicle, to facilitate an evaluation of a driver's performance, and toenable drivers who excessively allow their vehicle to idle to beidentified and trained to reduce their excess idle behavior. Theexcessive idle metric can be used as a driver performance metric on itsown, or be combined with other metrics to generate a driver performancemetric.

Another aspect of the concepts disclosed herein is to monitor a loadplaced upon a vehicle's engine during a driver's operation of a vehicle.While related to RPM, load is not equivalent. An estimation of engineload is sometimes calculated by a vehicle ECU, and differentmanufacturers use different combinations of parameters to calculateengine load, including but not limited to throttle position, RPM,manifold pressure, air flow, temperature, air conditioning clutchstatus, power steering pressure, and transmission gear status. Whereengine load is increased without performing more useful work (i.e.,carrying more cargo), increased fuel use and engine wear result withouta net benefit. Drivers who habitually operate their vehicles underhigher engine loads than required consume unnecessary amounts of fuel.Thus the concepts disclosed herein encompass monitoring engine loadwhile a driver operates a vehicle, to facilitate an evaluation of adriver's performance, and to enable drivers who consistently over loadtheir vehicle's engines to be identified and trained to reduce theirover loading behavior. The engine load metric can be used as a driverperformance metric on its own, or be combined with other metrics togenerate a driver performance metric.

Calculation of an Exemplary Performance Ranking

The calculation of an exemplary performance ranking is described belowin regard to one exemplary embodiment. It should be recognized that thisapproach for determining the performance ranking is not intended to belimiting, but rather to be illustrative of one contemplated valuationscheme and performance ranking implementation. In the exemplaryperformance ranking, a relatively higher numerical ranking value isgenerally indicative of relatively poorer driver performance. Thus, inthis example, the lower the numerical performance ranking, the betterthe driver's performance.

In this example, the sensor data are collected for various metricscorresponding to vehicle acceleration, vehicle deceleration, vehicleidle time, and vehicle speed. Each minute of acceleration time will beassigned one point. Each minute of deceleration time will be assignedone point. Each minute of idle time will be assigned one point. In thisexample, the fleet operator is also particularly concerned with driverswho violate speed limits on the freeway. Thus, each minute where thevehicle speed exceeds 60 miles an hour while the driver is driving thevehicle on the freeway will result in five points (the fleet operatorweighting excessive vehicle speed five times greater than the othermetrics). The points are added together to achieve a combined total. Thetotal is then normalized to derive a numerical ranking value for thedriver. As noted above, the normalization can be based on eitherdistance driven or time of operation, or a combination thereof.

Calculation of a Work Based Performance Ranking Using GPS Derived SlopeData

The concepts disclosed herein encompass using data collected during theoperation of a vehicle to calculate a weight of the vehicle, and thenusing the calculated weight in a performance analysis of the vehicle.The novel vehicle weight calculation disclosed herein employs in partvehicle position data collected while a vehicle is moving. The positionmetric can be automatically determined using a global positioningsatellite (GPS) receiver installed in the vehicle, to track thevehicle's change in position over time. It should be recognized that theGPS system is but one of a plurality of different vehicle positionsensing technologies that can be employed.

Fundamentally, GPS systems calculate velocity in three components (X, Y,Z or N/S, E/W, and Up/Down) based on a Doppler shift of the GPSsatellite signals. Scalar speeds can then be calculated from those threecomponents. For example, absolute speed or actual vehicle speed can bedetermined, as well as ground speed based on the shortest distancebetween two points (i.e., based on distance as the crow flies).

Horizontal ground speed (V_(HGS)) can be calculated using the followingrelationship based on the Pythagorean theorem:

V _(HGS)=√{square root over (x ² +y ²)}  (1)

To calculate a grade (G) the vehicle is traveling over (as apercentage), one can take the Z/Up magnitude and divide it by thehorizontal ground speed, V_(HGS), which results in the followingrelationship:

$\begin{matrix}{G = \frac{100(Z)}{V_{HGS}}} & (2)\end{matrix}$

Replacing Z, x and y with directional vectors (such as Up for Z, Westfor x and North for y, recognizing that such directional vectors areexemplary, and may change based on the actual GPS data collected fromthe vehicle) results in the following relationship:

$\begin{matrix}{G = \frac{100({Up})}{\sqrt{W^{2} + N^{2}}}} & (3)\end{matrix}$

Once one has derived G as discussed above, very useful vehicleperformance metrics can be determined.

One exemplary use of the slope data is to determine the mass of thevehicle at that time. Mass is a useful metric that can be used as afeedback metric for controlling certain vehicle systems. Some vehicleengine control units (ECUs) use torque output, engine RPMs, and vehiclevelocity to calculate a vehicle's mass or weight (as used herein and inthe claims that follow, the terms mass and weight are used synonymously,because on the surface of the Earth, the acceleration due to gravity(the “strength of gravity”) is approximately constant; thus the ratio ofthe weight force of a motionless object on the surface of the Earth toits mass is almost independent of its location, so that an object'sweight force can stand as a proxy for its mass, and vice versa). ThatECU weight/mass determination technique is error prone, because it doesnot take into account any slope conditions. Even though the ECUweight/mass determination technique is error prone, the ECU weight/massdetermination technique mass estimation provides a metric that can beused to as a feedback metric for various vehicle systems, includingtransmission shift points, engine RPM control, and emission controls.Having more accurate mass metrics (by including the GPS derived slopedata in a mass calculation) will provide an improved mass metric. Theconcepts disclosed herein specifically encompass a GPS unit configuredto use GPS data to calculate slope, to use the slope data and othervehicle parameters to determine vehicle mass (generally as discussedbelow), and then to provide a GPS slope based vehicle mass metric to avehicle ECU to be used as a metric to control vehicle operation.

Vehicle mass can also be used as an analytical metric to determine howmuch work a vehicle has performed. Being able to accurately determinethe amount of work a vehicle is performing will help vehicle operatorsanalyze their use patterns to seek out efficiency improvements. The GPSderived slope data enables more accurate vehicle mass calculations to bedetermined, which in turn will provide an improved vehicle performancedata set that can be mined to identify areas in which efficiencyimprovements could be made.

Having described the GPS based slope determination technique; thefollowing relationships will be used to obtain vehicle mass from the GPSdetermined slope (G). These relationships define the forces acting onthe vehicle, and include a force related to a grade the vehicle istraveling over, an aerodynamic force, a frictional force, and a forceapplied by the vehicle to overcome the forces acting on the vehicle. Theunderstanding of these forces is not new, but what is novel aretechniques disclosed herein to measure certain parameters that are usedto calculate such forces. Being able to measure those parameters, suchas a grade the vehicle is traveling over, enables more accurate forcecalculations to be performed, which in turn enables a betterunderstanding of vehicle operational efficiency.

A first force opposing a motion of the vehicle relates to a grade orslope the vehicle is traveling over, which can be defined using therelationship of Equation (4).

f _(grade) =m*g*G  (4)

where m is vehicle mass and g is the local gravitational field (i.e.,Earth's gravity).

A second force opposing a motion of the vehicle relates to aerodynamicforces acting on the vehicle, which can be defined using therelationship of Equation (5).

f _(areo)=½*p*C _(d) *A*ν ²  (5)

where ρ is air density, C_(d) is the coefficient of drag of the vehicle,A is the frontal area of the vehicle, and ν is the vehicle velocity(which can be obtained from the vehicle databus or from GPS data).

A third force opposing a motion of the vehicle relates to frictionalforces acting on the vehicle, which can be defined using therelationship of Equation (6).

f _(Friction) =C _(rr) *m*g*(1−G)  (6)

where C_(rr) is the coefficient of rolling resistance of the vehicle(which is assumed to be constant), m is vehicle mass, g is the localgravitational field (i.e., Earth's gravity), and G is the slope, whichis calculated using the GPS data as discussed above.

The force generated by the vehicle to overcome these opposing forces canbe defined using the relationship of Equation (7).

$\begin{matrix}{f_{Applied} = \frac{\tau*\frac{N}{v}*\pi}{r}} & (7)\end{matrix}$

where τ is engine output torque (as measured at the vehicle by vehiclesensors/reported by the vehicle databus), N is engine RMPs (as measuredat the vehicle by vehicle sensors/reported by the vehicle databus), ν isthe vehicle velocity (which can be obtained from the vehicle databus orfrom GPS data), π is the mathematical constant defined as the ratio ofany circle's circumference to its diameter, and r is the radius of thevehicles tires.

Vehicle mass is a parameter in the grade and frictional forcerelationships, of Equations (4) and (6), respectively. The massparameter itself can be defined using the relationship of Equation (8).

$\begin{matrix}{m = \frac{f_{{Applied}\;} - f_{Friction} - f_{Grade} - f_{Aero}}{\alpha}} & (8)\end{matrix}$

where m is vehicle mass and α is acceleration (which can be obtainedfrom the vehicle databus or from GPS data). In an exemplary embodiment,acceleration is obtained from the vehicle sensors.

Equations (4)-(8) can be solved to obtain mass, as defined using therelationship of Equation (9).

$\begin{matrix}{m = \frac{f_{Applied} - f_{Aero}}{a + {g*( {C_{rr} + S - {C_{rr}*S}} )}}} & (9)\end{matrix}$

Note that the following parameters will be measured during vehicleoperation in order for mass to be calculated: velocity, torque, RPM.Those parameters, combined with the GPS derived slope data, and knownparameters (gravity, air density, rolling resistance, frontal area,drag, pi, and tire radius) can be used by a processor in the vehicle tocalculate mass. Velocity, torque, and RPM represent metrics that manyvehicles already measure during vehicle operation, and thus can beaccessed by tapping into a vehicle ECU or databus. The conceptsdisclosed herein specifically encompass a GPS unit (or other positionsensing unit) including a data port coupled to a vehicle ECU/data busand a processor configured to calculate a GPS derived slope metric(generally as discussed herein) and a vehicle mass metric (generally asdiscussed herein, using in part the GPS derived slope data).

Having an accurate mass parameter, determined in real-time during theoperation of the vehicle, will enable a more accurate measurement of thework being performed by the vehicle to be measured. The work metric canbe used as a driver performance metric on its own, or be combined withother metrics to generate a driver performance metric.

FIG. 8 is a flow chart showing method steps implemented in an exemplaryembodiment, where position data (such as GPS data) collected during theoperation of a vehicle is used to determine a slope or grade over whichthe vehicle is being operated. That grade then can be used to calculateother data, such as a weight or mass of the vehicle. In a block 120,three dimensional position data (longitude, latitude and elevation) iscollected during operation of the vehicle. In at least some embodiments,that data is conveyed from the vehicle to a remote computing site (i.e.,a monitoring/data storage location) in real-time. In other embodiments,the position data is stored in a memory in the vehicle, to be conveyedto a remote computing site at a later time. In a block 122, the positiondata is used to determine horizontal ground speed, using therelationship of Equation (1). In a block 124, the horizontal groundspeed and the position data is used to determine the slope or grade,using the relationships of Equations (2) and (3).

In an optional block 126, the slope data is used to calculate a mass ofthe vehicle, using the relationship of Equation (9). In at least someembodiments, the data processing of blocks 122, 124, and 126 areperformed by a remote computing device. however, the concepts disclosedherein encompass embodiments where some or all of the data processing ofblocks 122, 124, and 126 are performed by a processor or logic circuitin the vehicle. In at least some embodiments in which the dataprocessing is implemented in the vehicle, the mass parameter is used byan ECU in the vehicle to control operating parameters of the vehicle.For example, vehicle mass can be used as a feedback parameter forcontrolling vehicle operations, including engine speed (RPM),transmission gear selection, and fuel flow/mixture (to control vehicleemissions). While using vehicle mass data used as a feedback parameterfor controlling vehicle operations has been implemented before, thoseimplementations have not used GPS derived slope data as a parameter todetermine vehicle mass.

FIG. 9 graphically illustrates force vectors acting on a vehicle,including a frictional force, an aerodynamic force, a grade/sloperelated force, and a vehicle applied force used to overcome the opposingforces. The forces are discussed above in connection with Equations(4)-(7), and those relationships can be used to use GPS derived slopedata to calculate vehicle mass, as shown in Equation (9).

FIG. 10 is a flow chart showing method steps implemented in an exemplaryembodiment, where GPS derived slope data and vehicle mass datacalculated using GPS derived slope data position data are used toanalyze vehicle performance, to determine a cost per loaded mile.Because the concepts disclosed herein provide an improved vehicle massparameter that is determined while a vehicle is operating, the analysisof the work performed by the vehicle, and the cost per loaded milecalculation, are more accurate than have been heretofore obtainableusing data derived by existing techniques. In a block 120, threedimensional position data (longitude, latitude and elevation) iscollected during operation of the vehicle. As noted above, in at leastsome embodiments, that data is conveyed from the vehicle to a remotecomputing site (i.e., a monitoring/data storage location) in real-time,while in other embodiments, the position data is stored in a memory inthe vehicle, to be conveyed to a remote computing site at a later time.In a block 123, fuel use and mileage data is collected during theoperation of the vehicle. In a block 126, GPS derived slope data is usedto calculate vehicle mass, generally as discussed above. Then, in ablock 127, the mileage data, fuel use data, and vehicle mass data areused to slope data is used to calculate a cost per loaded mile metric.

Fleet operators recognize that cost per loaded is a metric that can beanalyzed to improve the efficiency of their fleets, by allowing driverperformance to be evaluated, and by allowing different routes to beanalyzed, to determine the actual cost of servicing a particular route.While these concepts are not new, what is new is the improved vehiclemass metric that can be calculate in real-time while the vehicle isoperated, or at a later, where the mass metric is available for theduration of the vehicle operating segment (or trip). Note that in theprior art, unless the vehicle was weighed during the trip (i.e., beforethe trip, after the trip, or during the trip at a scale stop) suchaccurate vehicle mass data was not available, so the cost per loadedmetric was error prone, or accurate based on data collected at only afew selected segments of the trip. The concepts disclosed herein arebased on collecting vehicle operational data (fuel use, mileage,position data, etc.) frequently (i.e., multiple times a minute, or atleast once every few minutes, such intervals being exemplary) so thatvehicle mass can be accurately calculated at almost any time during theoperation of a vehicle.

An exemplary data set collected from a vehicle will include time indexedposition data, engine RPM data, vehicle torque data, and vehicle speed.This data will be collected frequently (multiple times per minute, or aplurality of times over a ten minute period) while the vehicle isoperational. Note that vehicle speed can be determined by analyzingposition data over time, or can be measured using a vehicle sensor (notethat comparing vehicle speed obtained from vehicle sensors with vehiclespeed calculated based on GPS data can identify errors in the vehiclespeed sensor, which can be caused by incorrect tire sizes, or driveradded speed cheating devices designed to override vehicle speedgovernors). Such a data set can also include other types of vehicledata, including, but not limited to, data relating to the vehicletransmission (so drivers can be evaluated based on percentage of timespent in the most efficient gear), data relating to the vehicle brakingsystem (so drivers can be evaluated based on their braking behavior),data relating to the use of cooling fan overrides (so drivers can beevaluated based on how often they such an override that reduces fuelefficiency), data relating to idle time (so drivers can be evaluatedbased on percentage of time spent in wasting fuel by idling, noting thatidle time can be evaluated in light of position data, so that driversare not penalized for idling at traffic lights), data relating to theuse of a vehicle's cruise control system (so drivers can be evaluatedbased on percentage of time spent driving at fuel efficient speeds).Note this exemplary data set includes the data required to calculatevehicle mass using GPS derived slope data, generally as discussed above.

FIGS. 11A-11C graphically illustrate histograms that can be derivedusing the exemplary data set discussed above. These histograms can beused by fleet operators to evaluate the efficiency performance ofindividual drivers and/or individual vehicles, being operated overspecific routes (the routes being defined by the position data). EachFigure has a histogram based on a vehicle being operated with a heavyload, and a histogram based on a vehicle being operated with a lightload. In FIG. 11A, speed histograms show a percentage of time a vehicleis operated at specific speeds, enabling fleet operators to determinehow often the driver/vehicle is using the most efficient vehicle speeds.The histograms include bars that show load vs. speed, and time vs.speed. In FIG. 11B, RPM histograms show a percentage of time a vehicleis operated at specific RMP settings, enabling fleet operators todetermine how often the driver/vehicle is using the most efficientengine speeds. The histograms include bars that show load vs. RPM, andtime vs. RPM. In FIG. 11C, load histograms show a percentage of time avehicle is operated at specific load settings, enabling fleet operatorsto determine how often the driver/vehicle is placed under the mostdemanding loads. In exemplary embodiments, when appropriate, thehistograms of FIGS. 11A-11C can be generated using the vehicle masscalculated using GPS derived slope data, generally as discussed above.

FIGS. 12A and 12B graphically illustrate histograms that can be derivedusing the exemplary data set discussed above. These histograms can beused by fleet operators to evaluate the efficiency performance ofindividual drivers and/or individual vehicles, being operated overspecific routes (the routes being defined by the position data). Thehistogram of FIG. 12A includes bars that show the cost per loaded mileand MPG for 14 different trips (each trip being defined by a differentset of position data from the exemplary data set). The histogram of FIG.12B includes bars that show the cost per loaded mile and MPG for 14different trips, with the data being normalized to a 30 ton load. Inexemplary embodiments, the histograms of FIGS. 12 and 12B are generatedusing the vehicle mass calculated using GPS derived slope data,generally as discussed above.

Hosted Website for Tracking Driver Performance Data

One aspect of the concepts disclosed herein is a hosted website,enabling drivers and fleet operators to monitor the performance ofdrivers, based on data collected during the drivers operation of avehicle. In at least one embodiment, drivers can compare theirperformance metrics to their peers, although the concepts disclosedherein also encompass embodiments where individual drivers can only seetheir own performance scores. Fleet operators can use these performancemetrics as incentives, by linking driver pay with performance.

In general, one or more performance metrics are automatically collectedwhile a driver is operating a vehicle, and that data is used to generatea score or rating of the driver's performance. In at least oneembodiment, the score is normalized to enable driver scores from othertypes of vehicles to be compared. Then, the driver performance data isposted to the hosted website.

In at least one related embodiment, fleet operators will pay driversusing a mileage component (i.e., paying drivers a fixed rate per loadedmile), while also making additional payments to drivers meetingpredefined performance characteristics. The hosted website can be usedas a forum to enable drivers to track their progress in achieving theirpay for performance goals. Fleet operators will have a wide degree offreedom in designing pay for performance goals or campaigns. Forexample, Fleet A can design a campaign in which only drivers havingperformance scores in the top 10% of the fleet will earn performancepay. Fleet B can design a campaign in which only the top 25 scoring willearn performance pay during a particular campaign. Fleets can predefinethe amount of performance pay each driver can earn. Fleets can alsopredefine a performance pay pool, such that the share of the pool earnedby each driver is a function of the number of drivers meeting predefinedperformance goals for the campaign.

In at least one embodiment, the performance metric will be a work basedperformance metric whose calculation involves using vehicle massdetermined using GPS derived slope data, generally as discussed above.

It should be recognized that performance campaigns can be metricspecific (hard braking performance, idle time performance, such metricsbeing exemplary and not limiting), or can be based on a singlenormalized score (cost per loaded mile), but will share in common thecharacteristic of being implemented for a defined period of time.Drivers will learn to associate such campaigns with opportunities toincrease their pay by meeting the performance goals of individualcampaigns.

FIG. 13 is a flow chart showing method steps implemented in an exemplaryembodiment. In a block 62, the hosted website defines a campaign (notingthat the website host may be a fleet operator providing the website foronly their drivers, or the hosting website host may be offering thedriver performance campaign to drivers from multiple fleets). Parametersof the campaign being defined will likely include a duration of thecampaign, the prize or performance pay being offered, the eligible pooldrivers, and any rules governing disqualification (such as any safetyviolation or speeding ticket automatically disqualifying a driver),noting that such parameters are exemplary, and not limiting. Theconcepts disclosed herein encompass embodiments in which campaigns arefleet specific, such that only drivers from a specific fleet canparticipate in that campaign (in such an embodiment, the specific fleetis likely funding the prize, although some third party, such as afavored vendor, customer, or advertiser might offer to fund the prize).The concepts disclosed herein encompass embodiments in which campaignsare not fleet specific, such that drivers from multiple fleets canparticipate in that campaign (in such an embodiment, an advertiser ortransportation industry vendor might offer to fund the prize).

In at least one embodiment, the campaign duration is open ended, in thatthe hosted website will track a drivers' performance data over time, sothe driver can use that data to look for other employment opportunities.For example, fleets would likely compete among themselves for drivershaving a combination of experience and high performance rankings.

In a block 64, driver performance data is posted to the hosted website.The concepts disclosed herein encompass permutations and combinations ofthe following: embodiments in which fleet operators can view performancerankings for all of their drivers, but not drivers of other fleets,embodiments in which drivers can only view their own personalperformance ranking, embodiments in which drivers can view performanceranking for all of the drivers in their fleet, but not drivers of otherfleets, and very transparent embodiments, in which interested partiescan visit the website and peruse driver performance rankings with littlerestrictions.

In a block 66, at the end of the defined campaign, the winning driver(or drivers) are announced and paid a performance pay bonus.

As noted above, in some embodiments, campaign participants are limitedto drivers in a specific fleet (i.e., an intra-company or intra-fleetcampaign). In such embodiments, that fleet generally will be paying theperformance bonuses for the campaign. In other embodiments, campaignparticipants are not limited to drivers in only one specific fleet(i.e., an inter-company or inter-fleet campaign). In such an embodiment,a third party may be paying the performance bonuses for the campaign.For example, companies providing goods and services to the trucking orvehicle transportation industry may sponsor such a campaign foradvertising purposes. A particular fleet operator seeking to attract theattention of drivers in other fleets might also be a sponsor of aninter-company campaign. FIG. 14 is a block diagram indicating that block62 of FIG. 13 can encompass both intra-company campaigns, as indicatedin a block 68, as well as inter-company campaigns, as indicated in ablock 70. FIG. 15 is a block diagram indicating that the performancebonus can encompass both intra-company payouts, as indicated in a block74 (where those bonus funds are used only to pay drivers of a specificfleet), as well as inter-company payouts (where those bonus funds areused to pay any winning driver, regardless of fleet), as indicated in ablock 76.

In at least one aspect of the concepts disclosed herein, the performancemetric is designed to facilitate comparison of driver performance dataacross different fleets, and different vehicles. This will enableindividual campaigns to include more participating drivers, which inturn will bring in more advertising revenue to fund bigger performancebonuses. In at least one embodiment, such a metric is mutually agreedupon by a plurality of different fleet operators. Adoption of a commonperformance metric across multiple fleets will enable top performingdrivers to be able to show their cumulative performance scores to otherparticipating fleet operators, providing an additional tool for fleetsto use when evaluating potential new hires. Such a common performancemetric will also enable participating fleet operators to appear moreattractive as potential employers than non-participating fleetoperators, who will not be offering the drivers the potential of earningthe additional performance based income (i.e., income in addition to theindustry standard pay by the mile driver compensation).

The concepts disclosed herein encompass embodiments in which individualfleet operators host their own website, where driver rankings in thatfleet can be compared. In other embodiments, the website is hosted by athird party, and multiple fleet operators participate. The third partycan offset their costs for operating the website by chargingparticipating fleet operators a fee, and/or by advertising revenue. Insome embodiments, all driver performance data is displayed in ananonymous format, so that individual drivers cannot be identified unlessthe driver shares their user ID. In some embodiments, drivers can onlycompare their score with drivers in their own fleet, while in otherembodiments drivers can see the performance data of drivers in otherfleets.

FIG. 16 is a functional block diagram of various elements that can beemployed to implement the hosted driver performance website concept, inone exemplary embodiment. The elements includes a plurality of enrolledvehicles 148 a-148 c (noting that the concepts disclosed herein can beapplied to a different number of vehicles), a plurality of drivers 152a-152 c (noting that the concepts disclosed herein can be applied to adifferent number of drivers), a plurality of vehicle operators 156 a-156c (noting that the concepts disclosed herein can be applied to adifferent number of vehicle operators), and a remote monitoring service150. Each vehicle includes the components discussed above in connectionwith FIG. 3 (noting the number and types of sensors disclosed in FIG. 3are exemplary, and not limiting), enabling the vehicle to conveyperformance data from the vehicle to remote monitoring service 150,which monitors the performance data from each vehicle 148 a-148 c overtime to enable the driver's performance while operating that vehicle tobe evaluated. In an exemplary embodiment monitoring service 150generates a webpage (as indicated by webpages 154 a-154 c) for eachvehicle operator, so the vehicle operator can review the performancerankings of each of their drivers. It should be understand that theconcepts disclosed herein also encompass other website designs, and thewebpage per fleet is not the only possible model. In one embodiment,drivers will have their own webpage 154 d (alternatively, drivers canaccess the webpage for their specific fleet).

It should be understood that monitoring service 150 is implemented usinga remote computing device, and that the term remote computing device isintended to encompass networked computers, including servers andclients, in private networks or as part of the Internet. The monitoringof the vehicle/driver performance data and driver performance ranking bymonitoring service 150 can be performed by multiple different computingdevices, such that performance data is stored by one element in such anetwork, retrieved for review by another element in the network, andanalyzed by yet another element in the network.

FIG. 17 is an exemplary screen shot of a webpage accessed by a driver toreview his (or her) performance ranking It should be understood that theexemplary webpage of FIG. 17 is based on having a webpage upon whichdrivers for a specific fleet can view their individual scores, as wellas the scores of other drivers in their fleet. The concepts disclosedherein specifically encompass embodiments where drivers can view onlytheir own performance rankings, in which case a different webpage designwould be employed.

Referring to FIG. 17, a webpage 100 includes a first portion 102 thatenables a driver to select a specific driver from among a plurality ofdrivers. The driver identities can be made anonymous, as shown in FIG.17 (numbers, not names), or some fleets may wish to list drivers by name(noting that privacy and consent issues for such a scheme are notdiscussed herein). It should be understood that webpage 100 can beunique to only one driver, such that portion 102 is not required. Usingnumbers to identify drivers enables individual drivers to look at thescores of their peers, without being able to individually identify whichdriver obtained what score. The driver will likely only know his ownunique number, and thus will only be able to personally identify his orher own score. Webpage 100 also includes a results section 104, wheredetails of the selected driver's performance ranking are disclosed. Itshould be understood that the elements shown on webpage 100 can bedisplayed on different portions of the webpage, or on different webpagesand/or different websites, instead of together. Webpage 100 alsoincludes an ad section 112, where the website host can earn revenue bydisplaying advertising, and a performance tip section 106, where thewebsite host provides tips to the driver for improving their performanceranking.

Referring to first portion 102, a driver has selected driver numberZONA0047 (noting that any type of driver identification can beemployed), such that the data displayed in results section 104 andperformance tip section 106 relate to driver ZONA0047. As shown in FIG.17, results section 104 includes results from three different campaigns,recognizing that in some embodiments drivers will be participating inmultiple campaigns (although it should be recognized that the conceptsdisclosed herein encompass embodiments where drivers participate agreater number of campaigns, or fewer campaigns, including only a singlecampaign (noting the duration of the single campaign could span thedriver's career).

Referring to results section 104, exemplary (but not limiting)information displayed herein includes details on the campaign, whetherthe campaign is inter-company or intra-company, and the driver'sperformance ranking for that campaign. A visual guide to the driver'srelative performance is displayed using a plurality of fuel pump icons(where a greater number of fuel pump icons, or other graphical icons,indicates a better performance rating). As shown, webpage 100 is basedon displaying 10 fuel pump icons for each campaign (icons 108 a-108 c),enabling the driver's performance to be graphically displayed on a scaleof 1 to 10. Thus, a driver whose performance ranking is in the top80^(th) percentile would have 8 solid or un-shadowed fuel pumps.Recognizing that while only full icons are displayed in this example,partial fuel pump icons can be used as well, to provide fractionalratings, or numbers between 0 and 10 can be rounded up to the next wholenumber. Radio buttons 110 a-c can be used by the driver to selectperformance tips for the particular campaign to be displayed in section106.

With respect to webpage 100, it should be understood that the design ofwebpage 100 is intended to be exemplary, and different webpage designscan be employed; and further, that the data on webpage 100 can beprovided to the vehicle operator on more than one webpage. If desired,access to webpage 100 can be restricted only to the fleet operatoremployer the driver and the driver themself. However, providing otherdrivers access to webpage 100 will enable drivers to see how they rankcompared to their peers, encouraging drivers to compete amongstthemselves to collect the performance bonus available in campaigns.

Referring once again to section 104 of webpage 100, note that a firstcampaign (associated with radio button 110 a) is identified asINTRA-COMPANY RANKING. This campaign is a fleet sponsored campaign forall of the fleet drivers to compete with each other for a performancebonus. Driver ZONA0047 ranks as 83^(rd) out of 225 drivers, with lowernumbers indicating better performance (i.e., the top ranking driverwould be ranked as 1, noting that the fleet operator could have reversedthe logic, such that 225 was the top ranking driver). Being the 83^(rd)lowest ranking driver out of a fleet of 225 drivers places driverZONA0047 in the top 63% ((225−83)/(225*100)=63.11%). Six fuel pump icons108 a are filled in. The campaign parameters are summarized, indicatingthat drivers having rankings from 1-25 (i.e., the top 88.89%) across theentire fleet share in the bonus pool assigned to this campaign. DriverZONA0047 needs to increase his ranking from 83^(rd) to 25^(th) in orderto be eligible for a share in the bonus pool. Note that campaigns can beconfigured such that the top 25 drivers earn equal shares of the bonuspool, and campaigns can also be configured such that higher rankingdrivers (i.e., closer to #1) earn a proportionally larger share of thebonus pool.

Referring once again to section 104 of webpage 100, note that a secondcampaign (associated with radio button 110 b) is identified asINTRA-COMPANY CAMPAIGN XYZ. This campaign is a fleet sponsored campaignfor all of the fleet drivers to compete with each other for aperformance bonus. Driver ZONA0047 has no ranking indicated in thecampaign, thus all ten fuel pump icons 108 b are shadowed or empty. Thecampaign parameters are summarized, indicating that Campaign XYZincludes a bonus pool to be shared by the 10 fleet drivers having themost improved performance scores for December 2011. Driver ZONA0047 hasselected radio button 110 b so that performance tips and additionalinformation related to Campaign XYZ are displayed in section 106. Thoseperformance tips include a first tip indicating that late shiftingreduced Driver ZONA0047's performance ranking by 9.2% over the last 14days. A second performance tip indicates that hard breaking eventsreduced Driver ZONA0047's performance ranking by 5.3% over the last 19days. Finally, the last performance tip indicates that excessive idlingon Dec. 11, 2011 disqualified Driver ZONA0047 from Campaign XYZ. Itshould be recognized that the specific performance tips shown in section106 are intended to be exemplary, and not limiting. The actualperformance tips displayed will be related to the specific campaign, anddesigned to provide feedback to individual driver's to enable them toidentify behaviors that have reduced their performance ranking.

In some embodiments section 112 includes banner ads targeted to drivers.In other embodiments section 112 includes advertising from vendors whoare sponsoring specific campaigns, or who are sponsoring hosting of thedriver performance ranking website. The advertising can be a mixture ofthose different types, and other types of advertising.

Exemplary System Environment

FIG. 18 is a functional block diagram of an exemplary system employed toimplement some of the concepts disclosed herein. The functional blockdiagram illustrates exemplary components used in vehicles 128 that areenrolled in a vehicle/driver performance monitoring service, toimplement some of the method steps discussed above. An exemplaryvehicle/driver performance monitoring service is based on adding anoptional data buffer 136 (or other short-term memory storage) and abi-directional data link 134 to each enrolled vehicle (in an exemplary,but not limiting embodiment, the data buffer and data link are combinedinto a single component). It should be understood that the short-termmemory storage is not required for embodiments where the performancedata transmitted from the enrolled vehicle does not include operational,vehicle, or driver related data that must be briefly stored. In anexemplary embodiment, the data link is a combination radio frequency(RF) transmitter and receiver, although separate transmitters andreceivers could be used (note the term RF specifically encompassescellular telephone based data links). A data terminal can optionally beincluded in the vehicle to facilitate operator entry of information andoperator transmission of information that is presented to the operatoron a display within the vehicle. Data collected on a portable datacollection device during an inspection can also be uploaded through sucha data terminal, or independently by direct transmission to the remotemonitoring service. While RF data transmission represents an exemplaryembodiment, other types of data transmission could be employed. If thevehicle does not already include performance data/operational datacollecting components 130, such components are added. Most vehiclesmanufactured today include operational data collecting componentsalready, as many of today's vehicles are designed to use suchcontinuously generated operational data to control operation of thevehicle in real-time, and such vehicles generally include datacollecting components, data buses, and controllers that use theoperational data to control the operation of the vehicle. The vehicleincludes at least one processor 132 that performs the function ofmanaging the transmission of performance data from the vehicle to theremote monitoring service, according to one or more of the transmissionparadigms discussed herein. In embodiments where the performance dataincludes temporary storage of operational data, the processor alsoimplements the function of temporarily storing operational data fromcomponents 130 in data buffer 136 or other temporary storage, and usingbi-directional data link 134 to convey real-time performance data and/orthe buffered operational/performance data from the enrolled vehicle to aremote computing device 140 (which is used to analyze the performance ofthe vehicle and/or driver). It should be understood that those processorfunctions can be implemented by a single processor, or distributedacross multiple processors.

In some embodiments, an output 138 is also included, to provideinformation to the driver in a form that can be easily understood by thedriver. Output 138 can be implemented using a speaker providing anaudible output, and using a display providing a visual output. Note thatoutput 138 can be combined into a single component with the data bufferand the data link, so only a single additional component is added to thevehicle (recognizing that most vehicles already include the additionalrequired components, such as the operational data collecting componentsand the processor).

While not specifically shown in FIG. 18, in particularly preferredembodiments the vehicle is equipped with a GPS unit (exemplary GPS unitsare illustrated in FIGS. 6 and 20). In a related preferred embodimentthe processor, the GPS component, any buffer, and data link are combinedinto a single telematics device. Such a device will send GPS andvehicle/driver performance data to the remote computing device discussedbelow at a plurality of different times during the course of theoperation of the vehicle. In general, the telematics device willtransmit data at intervals ranging from as frequently as once every 5 to15 seconds, or as rarely as once every 5 minutes, recognizing that suchintervals can vary, and are intended to be exemplary, and not limiting.

As indicated in FIG. 18, remote computing device 140 (operated by themonitoring service) is logically coupled via a network 142 (such as theInternet) to a computing device 144 (such as a personal computer,tablet, or smart phone) accessible to a driver (in embodiments wheredriver performance rankings are shared with drivers, noting only onesuch driver device is shown in the Figure; however, the monitoringservice will likely be monitoring the performance of a plurality ofdrivers, each likely having access to a different computing device 144),and a remote computing device 146 accessible to a vehicle operator(noting that in at least some embodiments, the monitoring serviceperforms the monitoring function for a plurality of different vehicleoperators/fleets). Network 142 facilitates communication betweencomputing devices 140, 144, and 146, enabling the monitoring service toefficiently communicate with drivers and vehicle operators. It should benoted that the concepts disclosed herein encompass embodiments where themonitoring service and vehicle operator are the same entity.

The concepts disclosed herein are in at least some embodiments intendedto be used by fleet owners operating multiple vehicles, and theperformance data conveyed to the remote location for diagnosis willinclude an ID component that enables each enrolled vehicle to beuniquely identified.

Exemplary Computing Environment

FIG. 19 is a functional block diagram of an exemplary computing devicethat can be employed to implement some of the method steps disclosedherein. It should be understood that the concepts disclosed hereinencompass processing of data collected at a vehicle both in the vehicleand at a remote computing device.

FIG. 19 schematically illustrates an exemplary computing system 250suitable for use in implementing the processing functions disclosedherein. Exemplary computing system 250 includes a processing unit 254that is functionally coupled to an input device 252 and to an outputdevice 262, e.g., a display (which can be used to output a result to auser, although such a result can also be stored). Processing unit 254comprises, for example, a central processing unit (CPU) 258 thatexecutes machine instructions for carrying out an analysis ofperformance data (and in some embodiments, of position data) collectedfrom enrolled vehicles, to identify mechanical faults in the enrolledvehicles. The machine instructions implement functions generallyconsistent with those described above. CPUs suitable for this purposeare available, for example, from Intel Corporation, AMD Corporation,Motorola Corporation, and other sources, as will be well-known to thoseof ordinary skill in this art.

Also included in processing unit 254 are a random access memory (RAM)256 and non-volatile memory 260, which can include read only memory(ROM) and may include some form of memory storage, such as a hard drive,optical disk (and drive), etc. These memory devices are bi-directionallycoupled to CPU 258. Such storage devices are well known in the art.Machine instructions and data are temporarily loaded into RAM 256 fromnon-volatile memory 260. Also stored in the non-volatile memory areoperating system software and ancillary software. While not separatelyshown, it will be understood that a generally conventional power supplywill be included to provide electrical power at voltage and currentlevels appropriate to energize computing system 250.

Input device 252 can be any device or mechanism that facilitates userinput into the operating environment, including, but not limited to, oneor more of a mouse or other pointing device, a keyboard, a microphone, amodem, or other input device. In general, the input device will be usedto initially configure computing system 250, to achieve the desiredprocessing (i.e., to monitor vehicle performance data over time todetect a mechanical fault). Configuration of computing system 250 toachieve the desired processing includes the steps of loading appropriateprocessing software into non-volatile memory 260, and launching theprocessing application (e.g., loading the processing software into RAM256 for execution by the CPU) so that the processing application isready for use. In embodiments where computing system 250 is implementedin a vehicle, the computing system 250 can be configured to runautonomously, such that a user input device not regularly employed.

Output device 262 generally includes any device that produces outputinformation, but will most typically comprise a monitor or computerdisplay designed for human visual perception of output. Use of aconventional computer keyboard for input device 252 and a computerdisplay for output device 262 should be considered as exemplary, ratherthan as limiting on the scope of this system. In embodiments wherecomputing system 250 is implemented in a vehicle, the computing system250 can be vehicle performance data (and position data when desired)collected in connection with operation of enrolled vehicles toconfigured to run autonomously, such that a user output device notregularly employed.

Data link 264 is configured to enable data to be input into computingsystem 250 for processing. Those of ordinary skill in the art willreadily recognize that many types of data links can be implemented,including, but not limited to, universal serial bus (USB) ports,parallel ports, serial ports, inputs configured to couple with portablememory storage devices, FireWire ports, infrared data ports, wirelessdata communication such as Wi-Fi and Bluetooth™, network connections viaEthernet ports, and other connections that employ the Internet.

Note that vehicle/driver performance data from the enrolled vehicleswill be communicated wirelessly in at least some embodiments, eitherdirectly to the remote computing system that analyzes the data toevaluate the driver's performance, or to some storage location or othercomputing system that is linked to computing system 250.

It should be understood that the term “remote computer” and the term“remote computing device” are intended to encompass a single computer aswell as networked computers, including servers and clients, in privatenetworks or as part of the Internet. The vehicle/driver performance datareceived by the monitoring service from the vehicle can be stored by oneelement in such a network, retrieved for review by another element inthe network, and analyzed by yet another element in the network. Whileimplementation of the method noted above has been discussed in terms ofexecution of machine instructions by a processor (i.e., the computingdevice implementing machine instructions to implement the specificfunctions noted above), the method could also be implemented using acustom circuit (such as an application specific integrated circuit orASIC).

The concepts disclosed herein encompass collecting data from a vehicleduring operation of the vehicle. The data collected is used to analyzethe performance of at least one of the driver and the vehicle. Inpreferred embodiments, the data is collected during operation of thevehicle and wirelessly transmitted from the vehicle during its operationto a remote computing device using a cellular phone network based datalink. The frequency of such data transmissions can be variedsignificantly. In general, more data is better, but data transmission isnot free, so there is a tension between cost and performance that issubject to variation based on an end user's needs and desires (someusers will be willing to pay for more data, while other users will wantto minimize data costs by limiting the quantity of data beingtransferred, even if that results in a somewhat lower quality data set).The artisan of skill will be able to readily determine a degree to whichdata quality can be reduced while still provide useful data set.

Exemplary GPS Device with Onboard Computing Environment

FIG. 20 is a functional block diagram of an exemplary telematics deviceadded to an enrolled vehicle to implement one or more of the methods ofFIGS. 1, 2, 7, 8 and 10.

An exemplary telematics unit 160 includes a controller 162, a wirelessdata link component 164 (an RF data link being exemplary, but notlimiting), a memory 166 in which data and machine instructions used bycontroller 162 are stored (again, it will be understood that a hardwarerather than software-based controller can be implemented, if desired), aposition sensing component 170 (such as a GPS receiver), and a datainput component 168 configured to extract vehicle data from thevehicle's data bus and/or the vehicle's onboard controller (noting thatthe single input is exemplary, and not limiting, as additional inputscan be added, and such inputs can be bi-directional to support dataoutput as well).

The capabilities of telematics unit 160 are particularly useful to fleetoperators. Telematics unit 160 is configured to collect position datafrom the vehicle (to enable vehicle owners to track the current locationof their vehicles, and where they have been) and to collect vehicleoperational data (including but not limited to engine temperature,coolant temperature, engine speed, vehicle speed, brake use, idle time,and fault codes), and to use the RF component to wirelessly convey suchdata to vehicle owners. The exemplary data set discussed above inconnection with calculated loaded cost per mile can also be employed.These data transmission can occur at regular intervals, in response to arequest for data, or in real-time, or be initiated based on parametersrelated to the vehicle's speed and/or change in location. The term“real-time” as used herein is not intended to imply the data aretransmitted instantaneously, since the data may instead be collectedover a relatively short period of time (e.g., over a period of secondsor minutes), and transmitted to the remote computing device on anongoing or intermittent basis, as opposed to storing the data at thevehicle for an extended period of time (hour or days), and transmittingan extended data set to the remote computing device after the data sethas been collected. Data collected by telematics unit 160 can beconveyed to the vehicle owner using RF component 164. If desired,additional memory can be included to temporarily store data id the RFcomponent cannot transfer data. In particularly preferred embodimentsthe RF components is GSM or cellular technology based.

In at least one embodiment, the controller is configured to implementthe method of FIG. 1 by using one or more of data collected from GPS 170and data from input 168. In a related embodiment, the controller isconfigured to implement the method of FIG. 2 by using one or more ofdata collected from GPS 170 and data from input 168. In yet anotherrelated embodiment, the controller is configured to implement steps ofthe method of FIG. 7.

In another embodiment, the controller is configured to implement stepsof the method of FIG. 8. Once the vehicle mass has been determined, thatdata can be added to GPS data that is transmitted to a remote computingdevice. In a related embodiment, input 168 is bi-directional, and thevehicle mass is output from the telematics device onto a vehicle databus, and can be used by an ECU to control vehicle operations. ECUs havebeen developed to use estimates of vehicle mass to control engine speedand transmission shifting, however, those estimates of vehicle mass havenot been based on GPS derived slope data, and as such those prior artvehicle mass estimations have been less accurate than the vehicle masscalculations based on GPS derived slope data as disclosed herein.

Newly Disclosed Subject Matter

FIG. 21 is a functional block diagram of an exemplary vehicle componentsemployed to implement the ECU reprogramming (in response to currentoperational data inputs) concepts disclosed herein.

FIG. 21 shows a vehicle 180 that includes a vehicle ECU 182, acontroller 184, a memory 186 (i.e., ECU Program Storage), an input 188,and a data link 190. As will be discussed below, certain of thesecomponents (such as the data link, the input, and the memory) can beomitted in various embodiments.

It should be understood that vehicle 180 can include more than one ECU182 whose programming can be changed to enhance the efficiency of thevehicle operation. In most cases, enhanced efficiency means better fuelefficiency, although it should be recognized that in certainapplications other factors, such as available horsepower, load rating,shift patterns (logic determining under what conditions an automatictransmission will change gears) RPM settings, and/or speed settings canbe changed by changing the ECU programming to suit current conditions.So while the concepts disclosed herein can be used to modify ECUprogramming based on current operating conditions to improve fuelefficiency, the concepts disclosed herein can also be used to optimizeperformance based on some other parameter than fuel efficiency. Itshould be understood that the concepts disclosed herein can be appliedto change ECU programming on more than one ECU in a vehicle.Furthermore, it should be understood that certain conditions mighttrigger multiple ECU reprogramming. For example, the detection of acertain predefined condition could trigger ECU programming changes thatmodify one or more of fuel flow settings, RPM settings, and transmissionshift patterns, noting that such parameters are exemplary and notlimiting.

In at least one embodiment, the ECU reprograming includes modifying afuel map.

Controller 184 is tasked with comparing the current operating conditionswith the current ECU programming, and determining if the ECU programmingshould be changed to optimize vehicle performance (according to somepredetermined characteristic, such as max fuel efficiency, max power,etc.). Controller 184 can implemented by a general purpose computingdevice executing machine instructions to implement such a task, or by acustom circuit designed to implement the specific function. In general,controller 184 is located at the vehicle, although it should berecognized that if controller 184 is logically coupled to a data link atthe vehicle, and the data link is logically coupled to ECU 182 (or avehicle data bus coupled to the vehicle ECU), that controller 184 can bedisposed at a location remote from the vehicle.

In embodiments where controller 184 is remote from the vehicle, input188 can be at the vehicle, and coupled to the data link in the vehiclecommunicating with controller 184. Alternatively, where controller 184is remote from the vehicle, a non-vehicle based input can be used by adispatcher/operator remote from the vehicle to provide input tocontroller 184.

Controller 184 can select a different ECU programming set based onempirical data indicating that a particular programming set is preferredbased on specific operating conditions, or based on a user knowledgebase that suggests that a certain programming set is more appropriatefor certain operating conditions (such suppositions based on userknowledge may or may not have been confirmed with empirical data).

In at least one embodiment, controller 184 of FIG. 21 and controller 162of FIG. 20 are the same controller. In at least one embodiment,controller 184 of FIG. 21 is part of a portable computing device used atthe vehicle. Such a portable computing device can be logically coupledto ECU 182 via a wired or wireless data link.

Memory 186 includes a plurality of different ECU programming sets, eachof which can be selected to optimize certain vehicle performancecharacteristics (such as fuel efficiency, speed settings, engine RPMsettings, available power settings, etc.) according to current operatingconditions at the vehicle (it should also be understood that theconcepts disclosed herein will enable operators to specifically selectparticular programming sets, irrespective of current conditions).Further, it should be understood that current operating conditions canbe based on current sensor data, as well as a specific input before suchsensor data is collected (such specific inputs can include a specificroute the vehicle will be following, or a particular load the vehiclewill be carrying).

In general, memory 186 is located at the vehicle, although it should berecognized that if memory 186 is logically coupled to a data link at thevehicle, and the data link is logically coupled to controller 184 at thevehicle, that memory 186 can be disposed at a location remote from thevehicle. Further, if controller 184 is located at a remote location,memory 186 need not be at the vehicle, but at some location wherecontroller 184 can access memory 186.

Input 188 represents an input device at the vehicle, which is used toconvey one or more data inputs to controller 184. In some embodiments,input 188 is a sensor disposed at the vehicle, which detects an ambientcondition, so that controller 184 can determine if the current ECUprogramming is appropriate for the current conditions. Exemplary sensorsinclude temperature sensors, GPS devices (including GPS devices that areconfigured to provide GPS derived slope data and vehicle mass data,generally as discussed above), speed sensors, inclinometers, weightsensors, oil pressure sensors, oil temperature sensors, coolanttemperature sensors, tire temperature sensors, and tire pressuresensors. It should be recognized that the concepts disclosed hereinencompass the use of only a single sensor input, as well as embodimentswherein controller 184 receives multiple different sensors inputs, andselects an ECU programming set based on multiple sensor inputs. In suchan embodiment, controller 184 can be configured to assign differentpriorities to different sensor inputs.

In at least one embodiment, input 188 is a controller or processor inthe vehicle that detects a condition, and conveys an indication of thatcondition to controller 184. For example, a processor in the vehiclemight detect that the vehicle is idling, and may be likely to continueidling for an extended period, such that controller 184 modifies ECUprogramming to reduce fuel consumption during a period of extended idleconditions. One technique for detecting or predicting extended idleconditions is based on historical data (vehicle regularly idles at thesame time or location), as well as using location data (i.e., a terminalor loading facility where a vehicle might logically idle while waitingto be loaded). Furthermore, if a processor detects that a vehicle is inneutral or park, and running for more than 90 seconds (or some otherpredetermined period of time), that processor could indicate tocontroller 184 that an idle state has been entered. Another techniquefor detecting an idle condition is determining if a PTO unit has beenactive. Activation of such a unit often indicates that the engine isbeing used to drive the PTO rather than for over the road operation,such that different ECU programming sets can be implemented to reducefuel consumption (as PTO units generally require much less horsepowerthan over the road travel).

In at least one embodiment, input 188 is (or is logically coupled to) aPTO, such that activation of the PTO triggers a change in ECUprogramming. As noted above, PTO units generally require much lesshorsepower than over the road travel.

In at least one embodiment, input 188 is a user interface that a vehicleoperator can use to either convey an indication of a specific conditionto controller 184, or to specifically request a particular ECUprogramming change. In at least one embodiment, the vehicle operatoruses input 188 to tell controller 184 that the vehicle will be enteringan extended idle state (for example, due to traffic conditions, loadingwait times, PTO use, or some other condition that requires the driverkeep the vehicle running but not moving, or moving at very low speeds).Controller 184 can then respond by selecting ECU programming thatreduces available horsepower but maximized fuel efficiency.

In at least one embodiment, the vehicle operator uses input 188 to tellcontroller 184 that the vehicle will be entering a jurisdiction orregion where a different max speed setting needs to be implemented.Controller 184 can then respond by selecting ECU programming thatmatches the speed setting to the jurisdiction or region selected by thedriver.

In one exemplary embodiment, the vehicle operator enters into a businessrelationship with a third party. The third party modifies a vehicle asindicated in FIG. 21 (noting that one or more of the elements shown inFIG. 21 can be implemented remotely, and not all elements are requiredin all implementations). The vehicle operator will contact the thirdparty when an ECU programming change is required. The third party willthen instruct controller 184 to execute the programming change. Whilesuch a business model can be implemented for any of the ECU programmingchanges disclosed herein, it should be noted that ECU programmingchanges based on different speed settings for different jurisdictionsrepresents a particularly interesting business opportunity. Currently,Canadian rules require trucks operating in Canada to have differentspeed settings than trucks operating in the US. When a truck with speedsettings configured for US operation enters Canada, drivers must taketheir vehicle to a repair facility for manual ECU reprogramming by atechnician. Such visits to repair facilities are time consuming andcostly. In the business arrangement noted above, the third party canimplement such a programming change much more efficiently, saving thetruck operator time and money. Such a business model can be anenhancement to vehicle monitoring services already offered by the thirdparty.

In at least one embodiment, the vehicle operator uses input 188 to tellcontroller 184 that the vehicle will be entering an uphill segment of aroute (generally this will be implemented where the uphill segment isrelatively long, as there may be minimal benefit to implementing an ECUprogramming change for a shorter segment). Controller 184 can thenrespond by selecting ECU programming that is more suited to largerhorsepower requirements for uphill travel.

In at least one embodiment, the vehicle operator uses input 188 to tellcontroller 184 that the vehicle will be entering a downhill segment of aroute (generally this will be implemented where the downhill segment isrelatively long, as there may be minimal benefit to implementing an ECUprogramming change for a shorter segment). Controller 184 can thenrespond by selecting ECU programming that is more suited to reducedhorsepower requirements for downhill travel (or increased braking, or achange in shift patterns more suited to downhill travel).

In at least one embodiment, the vehicle operator uses input 188 to tellcontroller 184 the current load conditions for the vehicle (for example,a weight of the material loaded onto the vehicle). Controller 184 canthen respond by selecting ECU programming that is more suited to thecurrent load.

In at least one embodiment, the vehicle operator uses input 188 to tellcontroller 184 the specific route for the vehicle). Controller 184 canthen respond by selecting ECU programming that is more suited to thatroute. In some embodiments, the controller is coupled to a GPS unit, sothat different ECU parameters can be implemented for different portionsof the route. Vehicle operators that continually traverse the same routeunder similar load conditions can perform empirical studies to determineoptimal ECU programming patters for different portions of that route.

In at least one embodiment, the vehicle operator uses input 188 to tellcontroller 184 the current road conditions (for example, elevation,night time operation, day time operation, heavy traffic conditions, thepresence of snow or ice, relatively cold ambient temperatures, orrelatively hot ambient temperatures). Controller 184 can then respond byselecting ECU programming that is more suited to the current roadconditions. As discussed above, empirical data or user knowledge can beused to determine which ECU programming sets are most suited to certainconditions.

In at least one embodiment, the vehicle operator uses input 188 to tellcontroller 184 the identity of the current operator. Controller 184 canthen respond by selecting ECU programming that is more suited to theskill set of the current driver. For example, fleet operators mayunderstand that relatively less experienced drivers are less able tooperate vehicles at peak fuel efficiency, and certain ECU programmingcan be used to manage their relative inefficiencies. More experienceddrivers may be able to achieve better fuel efficiency with different ECUprogramming. As discussed above, empirical data or user knowledge can beused to determine which ECU programming sets are most suited to certaindrivers.

Data link 190 can be used in lieu of or in addition to input 188 as amechanism to provide data input to controller 184, to prompt controller184 to review current ECU programming in light of the data input inorder to determine if ECU programming should be changed (it should beunderstood that the concepts disclosed herein also encompass embodimentsin which the data input is actually an instruction to change the ECUprogramming, such that to controller 184 does not need to analyze theinput to determine if a change is required, but simply executes theinstructed ECU programming change).

In embodiments wherein controller 184 is disposed at the vehicle, datalink 190 can be used to send data acquired remotely to controller 184.In embodiments wherein controller 184 is disposed remote from thevehicle (not separately shown), a similar data link at the vehicle canbe logically coupled to ECU 182, so that controller 184 can reprogramthe ECU remotely.

In at least one embodiment, data link 190 receives input from acontroller or processor remote from the vehicle that detects acondition, and conveys an indication of that condition to controller 184via the data link. For example, a processor remote from the vehiclemight detect that the vehicle is idling, and may be likely to continueidling for an extended period, such that controller 184 modifies ECUprogramming to reduce fuel consumption during a period of extended idleconditions. One technique for detecting or predicting extended idleconditions is based on historical data (GPS data acquired from thevehicle and sent to the remote processor indicates the vehicle iscurrently at a location that past data indicates is associated withextended idle conditions, or time records available to the remoteprocessor indicates the vehicle regularly idles at a particular point intime. Furthermore, if the remote processor (using data conveyed from thevehicle) detects that a vehicle is in neutral or park, and running formore than 90 seconds (or some other periods of time), that processorcould indicate to controller 184 that an idle state has been entered.Another technique for detecting an idle condition is determining if aPTO unit has been active (again, using data conveyed from the vehicle tothe remote processor). Activation of such a unit often indicates thatthe engine is being used to drive the PTO rather than for over the roadoperation, such that different ECU programming sets can be implementedto reduce fuel consumption (as PTO units generally require much lesshorsepower than over the road travel). It might seem that sending datafrom the vehicle to a remote processor, which analyzes the data todetect conditions that may indicate ECU programming changes should beimplemented is based on data transfer that could be eliminated by havingthe processor detecting the condition at the vehicle. While that istrue, collecting data from a vehicle and conveying that data to a remotesite for storage and analysis is a legitimate business model; and suchdata transmission already occurs. Performing analysis remote from thevehicle reduces the computational load at the vehicle, so if the data isalready being sent from the vehicle to a remote server, processing thedata remotely is a functional alternative to analyzing data at thevehicle. Such offsite processing is also useful in embodiments wherecontroller 184 is located remote from the vehicle.

In at least one embodiment, data link 190 receives input from a weatherservice that provides weather information for the vehicles generallocation. Controller 184 can then determine if current ECU programmingmatches the weather conditions from the weather service, and if not,controller 184 selects more appropriate ECU programming sets andreprograms the ECU.

In at least one embodiment, data link 190 receives input from a userinterface disposed remote from the vehicle that a dispatcher can use toeither convey an indication of a specific condition to controller 184,or to specifically request a particular ECU programming change. In atleast one embodiment, the dispatcher uses the remote input device anddata link 190 to tell controller 184 that the vehicle will be entering ajurisdiction or region where a different max speed setting needs to beimplemented. Controller 184 can then respond by selecting ECUprogramming that matches the speed setting to the jurisdiction or regionselected by the driver.

In at least one embodiment, data link 190 receives input from a userinterface disposed remote from the vehicle that a dispatcher can use totell controller 184 the current load conditions for the vehicle (forexample, a weight of the material loaded onto the vehicle). Dispatcherswill often have access to that information even though they are remotefrom the vehicle. Controller 184 can then respond by selecting ECUprogramming that is more suited to the current load.

In at least one embodiment, data link 190 receives input from a userinterface disposed remote from the vehicle that a dispatcher can use totell controller 184 the specific route for the vehicle. Controller 184can then respond by selecting ECU programming that is more suited tothat route. In some embodiments, the controller is coupled to a GPSunit, so that different ECU parameters can be implemented for differentportions of the route. Vehicle operators that continually traverse thesame route under similar load conditions can perform empirical studiesto determine optimal ECU programming patters for different portions ofthat route.

In at least one embodiment, data link 190 receives input from a userinterface disposed remote from the vehicle that a dispatcher can usetell controller 184 the identity of the current operator (dispatchersoften have access to driver data even though they are remote from thevehicle). Controller 184 can then respond by selecting ECU programmingthat is more suited to the skill set of the current driver. For example,fleet operators may understand that relatively less experienced driversare less able to operate vehicles at peak fuel efficiency, and certainECU programming can be used to manage their relative inefficiencies.More experienced drivers may be able to achieve better fuel efficiencywith different ECU programming. As discussed above, empirical data oruser knowledge can be used to determine which ECU programming sets aremost suited to certain drivers.

FIG. 22 is a flow chart showing exemplary method steps implementedaccording to one aspect of the concepts disclosed herein, in whichvehicle ECU programming is modified in response to one or more currentvehicle operational data inputs.

In a block 190, input data is conveyed to a processor tasked withevaluating ECU programming in light of the input data. In someembodiments, the processor is located at the vehicle, while in otherembodiments the processor is spaced apart from the vehicle, and islogically coupled to the vehicle ECU (or a data bus coupled to the ECU)via a wireless data link.

In a block 192, the processor tasked with evaluating current ECUprogramming in light of the input data determines if more efficient ECUprogramming sets exist. If not, the logic loops back to block 190. If inblock 192 it is determined that a better ECU programming set exists,then in a block 194 the current ECU programming is replaced with themore efficient ECU programming (based on the input from block 190).

As noted above, either empirical data or user knowledge can be used toassign specific ECU programming sets to specific conditions/inputs. Thusin block 192 the processor simply matches the available ECU programmingsets to the input.

In an exemplary embodiment, block 192 is implemented using the followingsteps. A plurality of different ECU programming sets are provided Foreach ECU programming set, the current vehicle operational data is usedas an input, and based on those inputs at least one vehicle performancecharacteristic that would result in using that ECU programming set isdetermined in light of current vehicle operational data. Note that insome embodiments, the output of using the current vehicle operationaldata and a specific ECU programming set is a single vehicle performancecharacteristic (such as horsepower or fuel economy), while in otherembodiments the output includes a plurality of different vehicleperformance characteristics. The determination as to whether the outputshould include a single or multiple vehicle performance characteristicsis based on what the vehicle operator hopes to achieve. In manyembodiments, the vehicle operator will be hoping to achieve improvedfuel economy, thus in at least some embodiments the only output will bean indication of what fuel economy will be achieved based on currentvehicle operational data inputs and a particular set of ECU programming.Other vehicle operators may want to emphasize fuel economy, subject tocertain other limitations involving factors such as speed, horsepower,or engine load. For example, max fuel economy under certain conditionsmight be achieved by using an unacceptably low vehicle speed. In otherconditions, max fuel economy may result when placing an unacceptable(for that operator at least) load on the engine (which may lead toreduced service life), so engine load may be an output important to someusers. The specific output (or outputs) will also be related to theparameters (or parameters) that are being optimized. Most users will notreally want maximum fuel economy, because to achieve max fuel economy anunrealistically low vehicle speed will be required (thus, even when fueleconomy is a parameter to be optimized, one really is seeking todetermine which ECU programming set will deliver max fuel economy atcurrent conditions while maintaining vehicle speed, engine load, and/orengine RPMs within certain ranges selected by the user, such parametersbeing exemplary, not limiting). While considering ECU programming setsin light of optimizing fuel economy will likely be important to manyusers, some users (such as racing car drivers) may want to comparedifferent ECU programming sets in light of optimizing horsepower (otherusers might want to optimize torque, or top speed). Thus, the outputwill be related to some predetermined vehicle characteristic that oneseeks to optimize.

Thus block 192 involves comparing the results from each ECU programmingset based on current vehicle operating conditions to identify the ECUprogramming set that optimizes vehicle performance based onpredetermined parameters. Then it is determined if the current ECUprogramming set is the ECU programming set that that optimizes vehicleperformance based on the predetermined parameters. It should beunderstood that the functions of block 192 can be implemented by aprocessor implementing suitable machine instructions, or a customcircuit,

FIG. 23 is a flow chart showing exemplary method steps implementedaccording to one aspect of the concepts disclosed herein, in whichvehicle ECU programming is modified in response to a specific userrequest for a programming change.

In a block 196, a driver, a remote dispatcher, or processor determinesthat current ECU programming needs to be changed. Often such a changewill be directed to changing speed limiting settings due to crossing ajurisdictional border, although changes for other purposes areencompassed by the concepts disclosed herein.

In a block 198, the current ECU programming set is changed as specifiedin block 196.

Although the concepts disclosed herein have been described in connectionwith the preferred form of practicing them and modifications thereto,those of ordinary skill in the art will understand that many othermodifications can be made thereto within the scope of the claims thatfollow. Accordingly, it is not intended that the scope of these conceptsin any way be limited by the above description, but instead bedetermined entirely by reference to the claims that follow.

The invention in which an exclusive right is claimed is defined by thefollowing:
 1. A method of changing ECU programming that limits maximumvehicle speed, the method, the comprising the steps of: (a) coupling awireless data link to a vehicle data bus logically coupled to the ECUresponsible for setting maximum vehicle speed; (b) enabling a driver ofthe vehicle to request changing the maximum vehicle speed setting; and(c) determining if the speed change request is approved; and (d) if thespeed change request is approved, reprogramming the ECU in the vehicleusing the new ECU programming to implement a new maximum vehicle speedsetting.
 2. The method of claim 1, further comprising the step ofconveying ECU programming to implement the new maximum vehicle speedfrom a remote computing device to the vehicle.
 3. The method of claim 1,further comprising the step of conveying the new maximum vehicle speedfrom a remote computing device to the vehicle.
 4. The method of claim 1,further comprising the step of conveying an approval for the new maximumvehicle speed from a remote computing device to the vehicle.
 5. Themethod of claim 4, wherein the approval is issued by a fleet operator.6. The method of claim 4, wherein the approval is issued by a thirdparty tasked with reviewing such approvals.
 7. The method of claim 1,wherein the step of determining if the speed change request is approvedis implemented by a processor at the vehicle that: (a) determines acurrent location of the vehicle using an input from a position sensingdevice at the vehicle; (b) determines a maximum speed setting for thecurrent location.
 8. The method of claim 7, wherein the step ofdetermining the maximum speed setting for the current location isimplemented by using a wireless data link to request the maximum speedsetting from a remote location.
 9. The method of claim 7, wherein thestep of determining the maximum speed setting for the current locationis implemented by consulting a non-transitory memory disposed at thevehicle.
 10. The method of claim 1, wherein the step of determining ifthe speed change request is approved is implemented by using a wirelessdata link to request the maximum speed setting from a remote location,wherein the remote location comprises at least one of: (a) a third partymonitoring service; and; (b) a fleet operator responsible for thevehicle.
 12. A system for changing programming for a vehicle controllerthat limits a maximum vehicle speed, the system comprising: (a) avehicle controller that limits a maximum vehicle speed; (b) a userinterface that enables a driver of the vehicle to request changing themaximum vehicle speed setting; and (c) an authorization controller thatdetermines if the speed change request is approved.
 13. The system ofclaim 12, wherein the authorization controller is a remote computingdevice, and further comprising a wireless data link logically coupled tothe user interface and the vehicle controller, the wireless data linkbeing used for conveying: (a) the driver request to the remoteauthorization controller; and (b) a response from the remoteauthorization controller to the vehicle controller.
 14. The system ofclaim 12, further comprising a position sensing device at the vehicle,and wherein the authorization controller is disposed at the vehicle, andimplements the functions of: (a) determining a current location of thevehicle using an input from the position sensing device at the vehicle;(b) determining a maximum speed setting for the current location. 15.The system of claim 13, wherein the authorization controller implementsthe function of determining the maximum speed setting for the currentlocation by consulting a non-transitory memory disposed at the vehicle.16. The system of claim 13, wherein the authorization controllerimplements the function of determining the maximum speed setting for thecurrent location by using a wireless data link to request the maximumspeed setting from a remote location, wherein the remote locationcomprises at least one of: (a) a third party monitoring service; and;(b) a fleet operator responsible for the vehicle.
 17. A method ofmatching the programming of a vehicle controller that controls at leastone performance characteristic of a vehicle to current vehicleoperational requirements, the comprising the steps of: (a) enabling auser to define current vehicle operational requirements; (b) using aprocessor to analyze the vehicle operational requirements in real-timeto determine if different programming would lead to improvedperformance, and (c) if the analysis so indicates, changing theprogramming of the vehicle controller to improve the performance of thevehicle.
 18. The method of claim 17, wherein the current vehicleoperational requirements are based on at least one element selected froma group consisting of: (a) vehicle mass; (b) vehicle speed; (c) a routethe vehicle will be traveling; (d) ambient temperature; (e) PTO use; (f)time of day; (g) ambient weather conditions; (h) initiation of anextended uphill route segment; (i) initiation of an extended downhillroute segment; (j) initiation of an extended idle condition; and (k)initiation of heavy traffic conditions.
 19. The method of claim 17,wherein the step of enabling a user to define current vehicleoperational requirements comprises the step of enabling a driver toinput the current vehicle operational requirements at the vehicle. 20.The method of claim 17, wherein the step of enabling a user to definecurrent vehicle operational requirements comprises the step of enablinga dispatcher to input the current vehicle operational requirements at alocation remote from the vehicle.